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1 Commits
user/rcade ... fix_path

Author SHA1 Message Date
Alexander Soare
d374873849 use Path type instead of str 2024-03-15 13:15:34 +00:00
193 changed files with 1166 additions and 4059 deletions
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1
.gitattributes vendored
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@@ -1,2 +1 @@
*.memmap filter=lfs diff=lfs merge=lfs -text
*.stl filter=lfs diff=lfs merge=lfs -text

751
.github/poetry/cpu/poetry.lock generated vendored
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@@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
[[package]]
name = "absl-py"
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]
[package.dependencies]
@@ -1772,31 +1699,6 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
xml = ["lxml (>=4.9.2)"]
[[package]]
name = "pettingzoo"
version = "1.24.3"
description = "Gymnasium for multi-agent reinforcement learning."
optional = false
python-versions = ">=3.8"
files = [
{file = "pettingzoo-1.24.3-py3-none-any.whl", hash = "sha256:23ed90517d2e8a7098bdaf5e31234b3a7f7b73ca578d70d1ca7b9d0cb0e37982"},
{file = "pettingzoo-1.24.3.tar.gz", hash = "sha256:91f9094f18e06fb74b98f4099cd22e8ae4396125e51719d50b30c9f1c7ab07e6"},
]
[package.dependencies]
gymnasium = ">=0.28.0"
numpy = ">=1.21.0"
[package.extras]
all = ["box2d-py (==2.3.5)", "chess (==1.9.4)", "multi-agent-ale-py (==0.1.11)", "pillow (>=8.0.1)", "pygame (==2.3.0)", "pymunk (==6.2.0)", "rlcard (==1.0.5)", "scipy (>=1.4.1)", "shimmy[openspiel] (>=1.2.0)"]
atari = ["multi-agent-ale-py (==0.1.11)", "pygame (==2.3.0)"]
butterfly = ["pygame (==2.3.0)", "pymunk (==6.2.0)"]
classic = ["chess (==1.9.4)", "pygame (==2.3.0)", "rlcard (==1.0.5)", "shimmy[openspiel] (>=1.2.0)"]
mpe = ["pygame (==2.3.0)"]
other = ["pillow (>=8.0.1)"]
sisl = ["box2d-py (==2.3.5)", "pygame (==2.3.0)", "pymunk (==6.2.0)", "scipy (>=1.4.1)"]
testing = ["AutoROM", "pre-commit", "pynput", "pytest", "pytest-cov", "pytest-markdown-docs", "pytest-xdist"]
[[package]]
name = "pillow"
version = "10.2.0"
@@ -2199,24 +2101,6 @@ tomli = {version = ">=1", markers = "python_version < \"3.11\""}
[package.extras]
testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
[[package]]
name = "pytest-cov"
version = "5.0.0"
description = "Pytest plugin for measuring coverage."
optional = false
python-versions = ">=3.8"
files = [
{file = "pytest-cov-5.0.0.tar.gz", hash = "sha256:5837b58e9f6ebd335b0f8060eecce69b662415b16dc503883a02f45dfeb14857"},
{file = "pytest_cov-5.0.0-py3-none-any.whl", hash = "sha256:4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652"},
]
[package.dependencies]
coverage = {version = ">=5.2.1", extras = ["toml"]}
pytest = ">=4.6"
[package.extras]
testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
[[package]]
name = "python-dateutil"
version = "2.9.0.post0"
@@ -2426,30 +2310,6 @@ urllib3 = ">=1.21.1,<3"
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
[[package]]
name = "robomimic"
version = "0.2.0"
description = "robomimic: A Modular Framework for Robot Learning from Demonstration"
optional = false
python-versions = ">=3"
files = [
{file = "robomimic-0.2.0.tar.gz", hash = "sha256:ee3bb5cf9c3e1feead6b57b43c5db738fd0a8e0c015fdf6419808af8fffdc463"},
]
[package.dependencies]
egl_probe = ">=1.0.1"
h5py = "*"
imageio = "*"
imageio-ffmpeg = "*"
numpy = ">=1.13.3"
psutil = "*"
tensorboard = "*"
tensorboardX = "*"
termcolor = "*"
torch = "*"
torchvision = "*"
tqdm = "*"
[[package]]
name = "safetensors"
version = "0.4.2"
@@ -2674,13 +2534,13 @@ test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov",
[[package]]
name = "sentry-sdk"
version = "1.43.0"
version = "1.42.0"
description = "Python client for Sentry (https://sentry.io)"
optional = false
python-versions = "*"
files = [
{file = "sentry-sdk-1.43.0.tar.gz", hash = "sha256:41df73af89d22921d8733714fb0fc5586c3461907e06688e6537d01a27e0e0f6"},
{file = "sentry_sdk-1.43.0-py2.py3-none-any.whl", hash = "sha256:8d768724839ca18d7b4c7463ef7528c40b7aa2bfbf7fe554d5f9a7c044acfd36"},
{file = "sentry-sdk-1.42.0.tar.gz", hash = "sha256:4a8364b8f7edbf47f95f7163e48334c96100d9c098f0ae6606e2e18183c223e6"},
{file = "sentry_sdk-1.42.0-py2.py3-none-any.whl", hash = "sha256:a654ee7e497a3f5f6368b36d4f04baeab1fe92b3105f7f6965d6ef0de35a9ba4"},
]
[package.dependencies]
@@ -2694,7 +2554,6 @@ asyncpg = ["asyncpg (>=0.23)"]
beam = ["apache-beam (>=2.12)"]
bottle = ["bottle (>=0.12.13)"]
celery = ["celery (>=3)"]
celery-redbeat = ["celery-redbeat (>=2)"]
chalice = ["chalice (>=1.16.0)"]
clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
django = ["django (>=1.8)"]
@@ -2939,58 +2798,9 @@ files = [
[package.dependencies]
mpmath = ">=0.19"
[[package]]
name = "tensorboard"
version = "2.16.2"
description = "TensorBoard lets you watch Tensors Flow"
optional = false
python-versions = ">=3.9"
files = [
{file = "tensorboard-2.16.2-py3-none-any.whl", hash = "sha256:9f2b4e7dad86667615c0e5cd072f1ea8403fc032a299f0072d6f74855775cc45"},
]
[package.dependencies]
absl-py = ">=0.4"
grpcio = ">=1.48.2"
markdown = ">=2.6.8"
numpy = ">=1.12.0"
protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
setuptools = ">=41.0.0"
six = ">1.9"
tensorboard-data-server = ">=0.7.0,<0.8.0"
werkzeug = ">=1.0.1"
[[package]]
name = "tensorboard-data-server"
version = "0.7.2"
description = "Fast data loading for TensorBoard"
optional = false
python-versions = ">=3.7"
files = [
{file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
{file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
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]
[[package]]
name = "tensorboardx"
version = "2.6.2.2"
description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
optional = false
python-versions = "*"
files = [
{file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
{file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
]
[package.dependencies]
numpy = "*"
packaging = "*"
protobuf = ">=3.20"
[[package]]
name = "tensordict"
version = "0.4.0+b4c91e8"
version = "0.4.0+6a56ecd"
description = ""
optional = false
python-versions = "*"
@@ -3011,7 +2821,7 @@ tests = ["pytest", "pytest-benchmark", "pytest-instafail", "pytest-rerunfailures
type = "git"
url = "https://github.com/pytorch/tensordict"
reference = "HEAD"
resolved_reference = "b4c91e8828c538ca0a50d8383fd99311a9afb078"
resolved_reference = "6a56ecd728757feee387f946b7da66dd452b739b"
[[package]]
name = "termcolor"
@@ -3274,23 +3084,6 @@ perf = ["orjson"]
reports = ["pydantic (>=2.0.0)"]
sweeps = ["sweeps (>=0.2.0)"]
[[package]]
name = "werkzeug"
version = "3.0.1"
description = "The comprehensive WSGI web application library."
optional = false
python-versions = ">=3.8"
files = [
{file = "werkzeug-3.0.1-py3-none-any.whl", hash = "sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10"},
{file = "werkzeug-3.0.1.tar.gz", hash = "sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc"},
]
[package.dependencies]
MarkupSafe = ">=2.1.1"
[package.extras]
watchdog = ["watchdog (>=2.3)"]
[[package]]
name = "zarr"
version = "2.17.1"
@@ -3314,13 +3107,13 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
[[package]]
name = "zipp"
version = "3.18.1"
version = "3.18.0"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
{file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
{file = "zipp-3.18.0-py3-none-any.whl", hash = "sha256:c1bb803ed69d2cce2373152797064f7e79bc43f0a3748eb494096a867e0ebf79"},
{file = "zipp-3.18.0.tar.gz", hash = "sha256:df8d042b02765029a09b157efd8e820451045890acc30f8e37dd2f94a060221f"},
]
[package.extras]
@@ -3330,4 +3123,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "8800bb8b24312d17b765cd2ce2799f49436171dd5fbf1bec3b07f853cfa9befd"
content-hash = "4aa6a1e3f29560dd4a1c24d493ee1154089da4aa8d2190ad1f786c125ab2b735"

12
.github/poetry/cpu/pyproject.toml vendored
View File

@@ -21,6 +21,7 @@ packages = [{include = "lerobot"}]
[tool.poetry.dependencies]
python = "^3.10"
cython = "^3.0.8"
termcolor = "^2.4.0"
omegaconf = "^2.3.0"
dm-env = "^1.6"
@@ -41,25 +42,22 @@ mpmath = "^1.3.0"
torch = {version = "^2.2.1", source = "torch-cpu"}
tensordict = {git = "https://github.com/pytorch/tensordict"}
torchrl = {git = "https://github.com/pytorch/rl", rev = "13bef426dcfa5887c6e5034a6e9697993fa92c37"}
mujoco = "^2.3.7"
mujoco = "^3.1.2"
mujoco-py = "^2.1.2.14"
gym = "^0.26.2"
opencv-python = "^4.9.0.80"
diffusers = "^0.26.3"
torchvision = {version = "^0.17.1", source = "torch-cpu"}
h5py = "^3.10.0"
dm = "^1.3"
dm-control = "1.0.14"
robomimic = "0.2.0"
dm-control = "^1.0.16"
huggingface-hub = "^0.21.4"
gymnasium-robotics = "^1.2.4"
gymnasium = "^0.29.1"
cmake = "^3.29.0.1"
[tool.poetry.group.dev.dependencies]
pre-commit = "^3.6.2"
debugpy = "^1.8.1"
pytest = "^8.1.0"
pytest-cov = "^5.0.0"
[[tool.poetry.source]]

124
.github/workflows/test.yml vendored
View File

@@ -1,4 +1,4 @@
name: Tests
name: Test
on:
pull_request:
@@ -10,15 +10,20 @@ on:
- main
jobs:
tests:
test:
if: |
${{ github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'CI') }} ||
${{ github.event_name == 'push' }}
runs-on: ubuntu-latest
env:
POETRY_VERSION: 1.8.2
POETRY_VERSION: 1.8.1
DATA_DIR: tests/data
TMPDIR: ~/tmp
TEMP: ~/tmp
TMP: ~/tmp
PYOPENGL_PLATFORM: egl
MUJOCO_GL: egl
LEROBOT_TESTS_DEVICE: cpu
steps:
#----------------------------------------------
# check-out repo and set-up python
@@ -81,10 +86,6 @@ jobs:
- name: Install dependencies
if: steps.restore-dependencies-cache.outputs.cache-hit != 'true'
env:
TMPDIR: ~/tmp
TEMP: ~/tmp
TMP: ~/tmp
run: |
mkdir ~/tmp
poetry install --no-interaction --no-root
@@ -109,126 +110,35 @@ jobs:
run: poetry install --no-interaction
#----------------------------------------------
# run tests & coverage
# run tests
#----------------------------------------------
- name: Run tests
env:
LEROBOT_TESTS_DEVICE: cpu
run: |
source .venv/bin/activate
pytest --cov=./lerobot --cov-report=xml tests
pytest tests
# TODO(aliberts): Link with HF Codecov account
# - name: Upload coverage reports to Codecov with GitHub Action
# uses: codecov/codecov-action@v4
# with:
# files: ./coverage.xml
# verbose: true
#----------------------------------------------
# run end-to-end tests
#----------------------------------------------
- name: Test train ACT on ALOHA end-to-end
- name: Test train pusht end-to-end
run: |
source .venv/bin/activate
python lerobot/scripts/train.py \
policy=act \
env=aloha \
wandb.enable=False \
offline_steps=2 \
online_steps=0 \
device=cpu \
save_model=true \
save_freq=2 \
horizon=20 \
policy.batch_size=2 \
hydra.run.dir=tests/outputs/act/
- name: Test eval ACT on ALOHA end-to-end
run: |
source .venv/bin/activate
python lerobot/scripts/eval.py \
--config tests/outputs/act/.hydra/config.yaml \
eval_episodes=1 \
env.episode_length=8 \
device=cpu \
policy.pretrained_model_path=tests/outputs/act/models/2.pt
# TODO(aliberts): This takes ~2mn to run, needs to be improved
# - name: Test eval ACT on ALOHA end-to-end (policy is None)
# run: |
# source .venv/bin/activate
# python lerobot/scripts/eval.py \
# --config lerobot/configs/default.yaml \
# policy=act \
# env=aloha \
# eval_episodes=1 \
# device=cpu
- name: Test train Diffusion on PushT end-to-end
run: |
source .venv/bin/activate
python lerobot/scripts/train.py \
policy=diffusion \
hydra.job.name=pusht \
env=pusht \
wandb.enable=False \
offline_steps=2 \
online_steps=0 \
device=cpu \
save_model=true \
save_freq=2 \
hydra.run.dir=tests/outputs/diffusion/
save_freq=1 \
hydra.run.dir=tests/outputs/
- name: Test eval Diffusion on PushT end-to-end
- name: Test eval pusht end-to-end
run: |
source .venv/bin/activate
python lerobot/scripts/eval.py \
--config tests/outputs/diffusion/.hydra/config.yaml \
eval_episodes=1 \
env.episode_length=8 \
device=cpu \
policy.pretrained_model_path=tests/outputs/diffusion/models/2.pt
- name: Test eval Diffusion on PushT end-to-end (policy is None)
run: |
source .venv/bin/activate
python lerobot/scripts/eval.py \
--config lerobot/configs/default.yaml \
policy=diffusion \
hydra.job.name=pusht \
env=pusht \
eval_episodes=1 \
device=cpu
- name: Test train TDMPC on Simxarm end-to-end
run: |
source .venv/bin/activate
python lerobot/scripts/train.py \
policy=tdmpc \
env=simxarm \
wandb.enable=False \
offline_steps=1 \
online_steps=1 \
device=cpu \
save_model=true \
save_freq=2 \
hydra.run.dir=tests/outputs/tdmpc/
- name: Test eval TDMPC on Simxarm end-to-end
run: |
source .venv/bin/activate
python lerobot/scripts/eval.py \
--config tests/outputs/tdmpc/.hydra/config.yaml \
eval_episodes=1 \
env.episode_length=8 \
device=cpu \
policy.pretrained_model_path=tests/outputs/tdmpc/models/2.pt
- name: Test eval TDPMC on Simxarm end-to-end (policy is None)
run: |
source .venv/bin/activate
python lerobot/scripts/eval.py \
--config lerobot/configs/default.yaml \
policy=tdmpc \
env=simxarm \
eval_episodes=1 \
device=cpu
policy.pretrained_model_path=tests/outputs/models/1.pt

4
.pre-commit-config.yaml
View File

@@ -14,11 +14,11 @@ repos:
- id: end-of-file-fixer
- id: trailing-whitespace
- repo: https://github.com/asottile/pyupgrade
rev: v3.15.2
rev: v3.15.1
hooks:
- id: pyupgrade
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.3.4
rev: v0.2.2
hooks:
- id: ruff
args: [--fix]

229
LICENSE
View File

@@ -253,31 +253,6 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
## Some of lerobot's code is derived from simxarm, which is subject to the following copyright notice:
MIT License
Copyright (c) 2023 Nicklas Hansen & Yanjie Ze
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
## Some of lerobot's code is derived from ALOHA, which is subject to the following copyright notice:
MIT License
@@ -301,207 +276,3 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
## Some of lerobot's code is derived from DETR, which is subject to the following copyright notice:
Apache License
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http://www.apache.org/licenses/
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455
README.md
View File

@@ -1,374 +1,72 @@
<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="media/lerobot-logo-thumbnail.png">
<source media="(prefers-color-scheme: light)" srcset="media/lerobot-logo-thumbnail.png">
<img alt="LeRobot, Hugging Face Robotics Library" src="media/lerobot-logo-thumbnail.png" style="max-width: 100%;">
</picture>
<br/>
<br/>
</p>
<div align="center">
[![Tests](https://github.com/huggingface/lerobot/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/test.yml?query=branch%3Amain)
[![Coverage](https://codecov.io/gh/huggingface/lerobot/branch/main/graph/badge.svg?token=TODO)](https://codecov.io/gh/huggingface/lerobot)
[![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE)
[![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
[![Version](https://img.shields.io/pypi/v/lerobot)](https://pypi.org/project/lerobot/)
[![Examples](https://img.shields.io/badge/Examples-green.svg)](https://github.com/huggingface/lerobot/tree/main/examples)
[![Discord](https://dcbadge.vercel.app/api/server/C5P34WJ68S?style=flat)](https://discord.gg/s3KuuzsPFb)
</div>
<h3 align="center">
<p>State-of-the-art Machine Learning for real-world robotics</p>
</h3>
---
🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier for entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulated environments so that everyone can get started. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there.
🤗 LeRobot hosts pretrained models and datasets on this HuggingFace community page: [huggingface.co/lerobot](https://huggingface.co/lerobot)
#### Examples of pretrained models and environments
<table>
<tr>
<td><img src="http://remicadene.com/assets/gif/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
<td><img src="http://remicadene.com/assets/gif/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
<td><img src="http://remicadene.com/assets/gif/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
</tr>
<tr>
<td align="center">ACT policy on ALOHA env</td>
<td align="center">TDMPC policy on SimXArm env</td>
<td align="center">Diffusion policy on PushT env</td>
</tr>
</table>
### Acknowledgment
- ACT policy and ALOHA environment are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha/)
- Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)
- TDMPC policy and Simxarm environment are adapted from [FOWM](https://www.yunhaifeng.com/FOWM/)
- Abstractions and utilities for Reinforcement Learning come from [TorchRL](https://github.com/pytorch/rl)
# LeRobot
## Installation
Download our source code:
```bash
git clone https://github.com/huggingface/lerobot.git
cd lerobot
Create a virtual environment with Python 3.10, e.g. using `conda`:
```
Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html):
```bash
conda create -y -n lerobot python=3.10
conda activate lerobot
```
Then, install 🤗 LeRobot:
```bash
python -m pip install .
[Install `poetry`](https://python-poetry.org/docs/#installation) (if you don't have it already)
```
curl -sSL https://install.python-poetry.org | python -
```
To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiments tracking, log in with
```bash
wandb login
Install dependencies
```
## Walkthrough
```
.
├── lerobot
| ├── configs # contains hydra yaml files with all options that you can override in the command line
| | ├── default.yaml # selected by default, it loads pusht environment and diffusion policy
| | ├── env # various sim environments and their datasets: aloha.yaml, pusht.yaml, simxarm.yaml
| | └── policy # various policies: act.yaml, diffusion.yaml, tdmpc.yaml
| ├── common # contains classes and utilities
| | ├── datasets # various datasets of human demonstrations: aloha, pusht, simxarm
| | ├── envs # various sim environments: aloha, pusht, simxarm
| | └── policies # various policies: act, diffusion, tdmpc
| └── scripts # contains functions to execute via command line
| ├── visualize_dataset.py # load a dataset and render its demonstrations
| ├── eval.py # load policy and evaluate it on an environment
| └── train.py # train a policy via imitation learning and/or reinforcement learning
├── outputs # contains results of scripts execution: logs, videos, model checkpoints
├── .github
| └── workflows
| └── test.yml # defines install settings for continuous integration and specifies end-to-end tests
└── tests # contains pytest utilities for continuous integration
```
### Visualize datasets
You can import our dataset class, download the data from the HuggingFace hub and use our rendering utilities:
```python
""" Copy pasted from `examples/1_visualize_dataset.py` """
import lerobot
from lerobot.common.datasets.aloha import AlohaDataset
from torchrl.data.replay_buffers import SamplerWithoutReplacement
from lerobot.scripts.visualize_dataset import render_dataset
print(lerobot.available_datasets)
# >>> ['aloha_sim_insertion_human', 'aloha_sim_insertion_scripted', 'aloha_sim_transfer_cube_human', 'aloha_sim_transfer_cube_scripted', 'pusht', 'xarm_lift_medium']
# we use this sampler to sample 1 frame after the other
sampler = SamplerWithoutReplacement(shuffle=False)
dataset = AlohaDataset("aloha_sim_transfer_cube_human", sampler=sampler)
video_paths = render_dataset(
dataset,
out_dir="outputs/visualize_dataset/example",
max_num_samples=300,
fps=50,
)
print(video_paths)
# >>> ['outputs/visualize_dataset/example/episode_0.mp4']
```
Or you can achieve the same result by executing our script from the command line:
```bash
python lerobot/scripts/visualize_dataset.py \
env=aloha \
task=sim_sim_transfer_cube_human \
hydra.run.dir=outputs/visualize_dataset/example
# >>> ['outputs/visualize_dataset/example/episode_0.mp4']
```
### Evaluate a pretrained policy
You can import our environment class, download pretrained policies from the HuggingFace hub, and use our rollout utilities with rendering:
```python
""" Copy pasted from `examples/2_evaluate_pretrained_policy.py`
# TODO
```
Or you can achieve the same result by executing our script from the command line:
```bash
python lerobot/scripts/eval.py \
--hub-id lerobot/diffusion_policy_pusht_image \
eval_episodes=10 \
hydra.run.dir=outputs/eval/example_hub
```
After launching training of your own policy, you can also re-evaluate the checkpoints with:
```bash
python lerobot/scripts/eval.py \
--config PATH/TO/FOLDER/config.yaml \
policy.pretrained_model_path=PATH/TO/FOLDER/weights.pth \
eval_episodes=10 \
hydra.run.dir=outputs/eval/example_dir
```
See `python lerobot/scripts/eval.py --help` for more instructions.
### Train your own policy
You can import our dataset, environment, policy classes, and use our training utilities (if some data is missing, it will be automatically downloaded from HuggingFace hub):
```python
""" Copy pasted from `examples/3_train_policy.py`
# TODO
```
Or you can achieve the same result by executing our script from the command line:
```bash
python lerobot/scripts/train.py \
hydra.run.dir=outputs/train/example
```
You can easily train any policy on any environment:
```bash
python lerobot/scripts/train.py \
env=aloha \
task=sim_insertion \
dataset_id=aloha_sim_insertion_scripted \
policy=act \
hydra.run.dir=outputs/train/aloha_act
```
## Contribute
Feel free to open issues and PRs, and to coordinate your efforts with the community on our [Discord Channel](https://discord.gg/VjFz58wn3R). For specific inquiries, reach out to [Remi Cadene](remi.cadene@huggingface.co).
### TODO
If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/orgs/huggingface/projects/46)
### Follow our style
```bash
# install if needed
pre-commit install
# apply style and linter checks before git commit
pre-commit
```
### Add dependencies
Instead of using `pip` directly, we use `poetry` for development purposes to easily track our dependencies.
If you don't have it already, follow the [instructions](https://python-poetry.org/docs/#installation) to install it.
Install the project with:
```bash
poetry install
```
Then, the equivalent of `pip install some-package`, would just be:
```bash
poetry add some-package
If you encounter a disk space error, try to change your tmp dir to a location where you have enough disk space, e.g.
```
mkdir ~/tmp
export TMPDIR='~/tmp'
```
**NOTE:** Currently, to ensure the CI works properly, any new package must also be added in the CPU-only environment dedicated to the CI. To do this, you should create a separate environment and add the new package there as well. For example:
```bash
# Add the new package to your main poetry env
poetry add some-package
# Add the same package to the CPU-only env dedicated to CI
conda create -y -n lerobot-ci python=3.10
conda activate lerobot-ci
cd .github/poetry/cpu
poetry add some-package
To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiments tracking, log in with
```
wandb login
```
### Run tests locally
Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
On Mac:
```bash
brew install git-lfs
git lfs install
```
On Ubuntu:
```bash
sudo apt-get install git-lfs
git lfs install
```
Pull artifacts if they're not in [tests/data](tests/data)
```bash
git lfs pull
```
When adding a new dataset, mock it with
```bash
python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
```
Run tests
```bash
DATA_DIR="tests/data" pytest -sx tests
```
### Add a new dataset
To add a dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
```bash
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
```
Then you can upload it to the hub with:
```bash
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
--repo-type dataset \
--revision v1.0
```
You will need to set the corresponding version as a default argument in your dataset class:
```python
version: str | None = "v1.0",
```
See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
For instance, for [lerobot/pusht](https://huggingface.co/datasets/lerobot/pusht), we used:
```bash
HF_USER=lerobot
DATASET=pusht
```
If you want to improve an existing dataset, you can download it locally with:
```bash
mkdir -p data/$DATASET
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download ${HF_USER}/$DATASET \
--repo-type dataset \
--local-dir data/$DATASET \
--local-dir-use-symlinks=False \
--revision v1.0
```
Iterate on your code and dataset with:
```bash
DATA_DIR=data python train.py
```
Upload a new version (v2.0 or v1.1 if the changes are respectively more or less significant):
```bash
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
--repo-type dataset \
--revision v1.1 \
--delete "*"
```
Then you will need to set the corresponding version as a default argument in your dataset class:
```python
version: str | None = "v1.1",
```
See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
## Usage
Finally, you might want to mock the dataset if you need to update the unit tests as well:
```bash
python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
```
### Add a pretrained policy
Once you have trained a policy you may upload it to the HuggingFace hub.
Firstly, make sure you have a model repository set up on the hub. The hub ID looks like HF_USER/REPO_NAME.
Secondly, assuming you have trained a policy, you need:
- `config.yaml` which you can get from the `.hydra` directory of your training output folder.
- `model.pt` which should be one of the saved models in the `models` directory of your training output folder (they won't be named `model.pt` but you will need to choose one).
- `stats.pth` which should point to the same file in the dataset directory (found in `data/{dataset_name}`).
To upload these to the hub, prepare a folder with the following structure (you can use symlinks rather than copying):
### Train
```
to_upload
├── config.yaml
├── model.pt
└── stats.pth
python lerobot/scripts/train.py \
hydra.job.name=pusht \
env=pusht
```
With the folder prepared, run the following with a desired revision ID.
### Visualize offline buffer
```bash
huggingface-cli upload $HUB_ID to_upload --revision $REVISION_ID
```
python lerobot/scripts/visualize_dataset.py \
hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
env=pusht
```
If you want this to be the default revision also run the following (don't worry, it won't upload the files again; it will just adjust the file pointers):
### Visualize online buffer / Eval
```bash
huggingface-cli upload $HUB_ID to_upload
```
python lerobot/scripts/eval.py \
hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
env=pusht
```
See `eval.py` for an example of how a user may use your policy.
## TODO
If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/users/Cadene/projects/1)
Ask [Remi Cadene](re.cadene@gmail.com) for access if needed.
### Improve your code with profiling
## Profile
An example of a code snippet to profile the evaluation of a policy:
**Example**
```python
from torch.profiler import profile, record_function, ProfilerActivity
@@ -387,12 +85,87 @@ with profile(
with record_function("eval_policy"):
for i in range(num_episodes):
prof.step()
# insert code to profile, potentially whole body of eval_policy function
```
```bash
python lerobot/scripts/eval.py \
--config outputs/pusht/.hydra/config.yaml \
pretrained_model_path=outputs/pusht/model.pt \
pretrained_model_path=/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/final.pt \
eval_episodes=7
```
## Contribute
**Style**
```
# install if needed
pre-commit install
# apply style and linter checks before git commit
pre-commit run -a
```
**Adding dependencies (temporary)**
Right now, for the CI to work, whenever a new dependency is added it needs to be also added to the cpu env, eg:
```
# Run in this directory, adds the package to the main env with cuda
poetry add some-package
# Adds the same package to the cpu env
cd .github/poetry/cpu && poetry add some-package
```
**Tests**
Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).
On Mac:
```
brew install git-lfs
git lfs install
```
On Ubuntu:
```
sudo apt-get install git-lfs
git lfs install
```
Pull artifacts if they're not in [tests/data](tests/data)
```
git lfs pull
```
When adding a new dataset, mock it with
```
python tests/scripts/mock_dataset.py --in-data-dir data/<dataset_id> --out-data-dir tests/data/<dataset_id>
```
Run tests
```
DATA_DIR="tests/data" pytest -sx tests
```
**Datasets**
To add a pytorch rl dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
```
huggingface-cli login --token $HUGGINGFACE_TOKEN --add-to-git-credential
```
Then you can upload it to the hub with:
```
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload --repo-type dataset $HF_USER/$DATASET data/$DATASET
```
For instance, for [cadene/pusht](https://huggingface.co/datasets/cadene/pusht), we used:
```
HF_USER=cadene
DATASET=pusht
```
## Acknowledgment
- Our Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)
- Our TDMPC policy and Simxarm environment are adapted from [FOWM](https://www.yunhaifeng.com/FOWM/)
- Our ACT policy and ALOHA environment are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha/)

24
examples/1_visualize_dataset.py
View File

@@ -1,24 +0,0 @@
import os
from torchrl.data.replay_buffers import SamplerWithoutReplacement
import lerobot
from lerobot.common.datasets.aloha import AlohaDataset
from lerobot.scripts.visualize_dataset import render_dataset
print(lerobot.available_datasets)
# >>> ['aloha_sim_insertion_human', 'aloha_sim_insertion_scripted', 'aloha_sim_transfer_cube_human', 'aloha_sim_transfer_cube_scripted', 'pusht', 'xarm_lift_medium']
# we use this sampler to sample 1 frame after the other
sampler = SamplerWithoutReplacement(shuffle=False)
dataset = AlohaDataset("aloha_sim_transfer_cube_human", sampler=sampler, root=os.environ.get("DATA_DIR"))
video_paths = render_dataset(
dataset,
out_dir="outputs/visualize_dataset/example",
max_num_samples=300,
fps=50,
)
print(video_paths)
# ['outputs/visualize_dataset/example/episode_0.mp4']

1
examples/2_evaluate_pretrained_policy.py
View File

@@ -1 +0,0 @@
# TODO

1
examples/3_train_policy.py
View File

@@ -1 +0,0 @@
# TODO

58
lerobot/__init__.py
View File

@@ -1,59 +1 @@
"""
This file contains lists of available environments, dataset and policies to reflect the current state of LeRobot library.
We do not want to import all the dependencies, but instead we keep it lightweight to ensure fast access to these variables.
Example:
```python
import lerobot
print(lerobot.available_envs)
print(lerobot.available_tasks_per_env)
print(lerobot.available_datasets_per_env)
print(lerobot.available_datasets)
print(lerobot.available_policies)
```
Note:
When implementing a concrete class (e.g. `AlohaDataset`, `PushtEnv`, `DiffusionPolicy`), you need to:
1. set the required class attributes:
- for classes inheriting from `AbstractDataset`: `available_datasets`
- for classes inheriting from `AbstractEnv`: `name`, `available_tasks`
- for classes inheriting from `AbstractPolicy`: `name`
2. update variables in `lerobot/__init__.py` (e.g. `available_envs`, `available_datasets_per_envs`, `available_policies`)
3. update variables in `tests/test_available.py` by importing your new class
"""
from lerobot.__version__ import __version__ # noqa: F401
available_envs = [
"aloha",
"pusht",
"simxarm",
]
available_tasks_per_env = {
"aloha": [
"sim_insertion",
"sim_transfer_cube",
],
"pusht": ["pusht"],
"simxarm": ["lift"],
}
available_datasets_per_env = {
"aloha": [
"aloha_sim_insertion_human",
"aloha_sim_insertion_scripted",
"aloha_sim_transfer_cube_human",
"aloha_sim_transfer_cube_scripted",
],
"pusht": ["pusht"],
"simxarm": ["xarm_lift_medium"],
}
available_datasets = [dataset for env in available_envs for dataset in available_datasets_per_env[env]]
available_policies = [
"act",
"diffusion",
"tdmpc",
]

2
lerobot/__version__.py
View File

@@ -1,4 +1,4 @@
"""To enable `lerobot.__version__`"""
""" To enable `lerobot.__version__` """
from importlib.metadata import PackageNotFoundError, version

74
lerobot/common/datasets/abstract.py
View File

@@ -9,74 +9,30 @@ import tqdm
from huggingface_hub import snapshot_download
from tensordict import TensorDict
from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
from torchrl.data.replay_buffers.samplers import Sampler
from torchrl.data.replay_buffers.samplers import SliceSampler
from torchrl.data.replay_buffers.storages import TensorStorage, _collate_id
from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
from torchrl.envs.transforms.transforms import Compose
HF_USER = "lerobot"
class AbstractDataset(TensorDictReplayBuffer):
"""
AbstractDataset represents a dataset in the context of imitation learning or reinforcement learning.
This class is designed to be subclassed by concrete implementations that specify particular types of datasets.
These implementations can vary based on the source of the data, the environment the data pertains to,
or the specific kind of data manipulation applied.
Note:
- `TensorDictReplayBuffer` is the base class from which `AbstractDataset` inherits. It provides the foundational
functionality for storing and retrieving `TensorDict`-like data.
- `available_datasets` should be overridden by concrete subclasses to list the specific dataset variants supported.
It is expected that these variants correspond to a HuggingFace dataset on the hub.
For instance, the `AlohaDataset` which inherites from `AbstractDataset` has 4 available dataset variants:
- [aloha_sim_transfer_cube_scripted](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_scripted)
- [aloha_sim_insertion_scripted](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_scripted)
- [aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human)
- [aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human)
- When implementing a concrete class (e.g. `AlohaDataset`, `PushtEnv`, `DiffusionPolicy`), you need to:
1. set the required class attributes:
- for classes inheriting from `AbstractDataset`: `available_datasets`
- for classes inheriting from `AbstractEnv`: `name`, `available_tasks`
- for classes inheriting from `AbstractPolicy`: `name`
2. update variables in `lerobot/__init__.py` (e.g. `available_envs`, `available_datasets_per_envs`, `available_policies`)
3. update variables in `tests/test_available.py` by importing your new class
"""
available_datasets: list[str] | None = None
class AbstractExperienceReplay(TensorDictReplayBuffer):
def __init__(
self,
dataset_id: str,
version: str | None = None,
batch_size: int | None = None,
batch_size: int = None,
*,
shuffle: bool = True,
root: Path | None = None,
pin_memory: bool = False,
prefetch: int = None,
sampler: Sampler | None = None,
collate_fn: Callable | None = None,
writer: Writer | None = None,
sampler: SliceSampler = None,
collate_fn: Callable = None,
writer: Writer = None,
transform: "torchrl.envs.Transform" = None,
):
assert (
self.available_datasets is not None
), "Subclasses of `AbstractDataset` should set the `available_datasets` class attribute."
assert (
dataset_id in self.available_datasets
), f"The provided dataset ({dataset_id}) is not on the list of available datasets {self.available_datasets}."
self.dataset_id = dataset_id
self.version = version
self.shuffle = shuffle
self.root = root if root is None else Path(root)
if self.root is not None and self.version is not None:
logging.warning(
f"The version of the dataset ({self.version}) is not enforced when root is provided ({self.root})."
)
self.root = root
storage = self._download_or_load_dataset()
super().__init__(
@@ -93,9 +49,9 @@ class AbstractDataset(TensorDictReplayBuffer):
@property
def stats_patterns(self) -> dict:
return {
("observation", "state"): "b c -> c",
("observation", "image"): "b c h w -> c 1 1",
("action",): "b c -> c",
("observation", "state"): "b c -> 1 c",
("observation", "image"): "b c h w -> 1 c 1 1",
("action",): "b c -> 1 c",
}
@property
@@ -129,7 +85,7 @@ class AbstractDataset(TensorDictReplayBuffer):
self._transform = transform
def compute_or_load_stats(self, num_batch=100, batch_size=32) -> TensorDict:
stats_path = self.data_dir / "stats.pth"
stats_path = Path(self.data_dir) / "stats.pth"
if stats_path.exists():
stats = torch.load(stats_path)
else:
@@ -140,14 +96,10 @@ class AbstractDataset(TensorDictReplayBuffer):
def _download_or_load_dataset(self) -> torch.StorageBase:
if self.root is None:
self.data_dir = Path(
snapshot_download(
repo_id=f"{HF_USER}/{self.dataset_id}", repo_type="dataset", revision=self.version
)
)
self.data_dir = snapshot_download(repo_id=f"cadene/{self.dataset_id}", repo_type="dataset")
else:
self.data_dir = self.root / self.dataset_id
return TensorStorage(TensorDict.load_memmap(self.data_dir / "replay_buffer"))
return TensorStorage(TensorDict.load_memmap(self.data_dir))
def _compute_stats(self, num_batch=100, batch_size=32):
rb = TensorDictReplayBuffer(

26
lerobot/common/datasets/aloha.py
View File

@@ -9,11 +9,11 @@ import torch
import torchrl
import tqdm
from tensordict import TensorDict
from torchrl.data.replay_buffers.samplers import Sampler
from torchrl.data.replay_buffers.samplers import SliceSampler
from torchrl.data.replay_buffers.storages import TensorStorage
from torchrl.data.replay_buffers.writers import Writer
from lerobot.common.datasets.abstract import AbstractDataset
from lerobot.common.datasets.abstract import AbstractExperienceReplay
DATASET_IDS = [
"aloha_sim_insertion_human",
@@ -80,27 +80,25 @@ def download(data_dir, dataset_id):
gdown.download(EP49_URLS[dataset_id], output=str(data_dir / "episode_49.hdf5"), fuzzy=True)
class AlohaDataset(AbstractDataset):
available_datasets = DATASET_IDS
class AlohaExperienceReplay(AbstractExperienceReplay):
def __init__(
self,
dataset_id: str,
version: str | None = "v1.2",
batch_size: int | None = None,
batch_size: int = None,
*,
shuffle: bool = True,
root: Path | None = None,
pin_memory: bool = False,
prefetch: int = None,
sampler: Sampler | None = None,
collate_fn: Callable | None = None,
writer: Writer | None = None,
sampler: SliceSampler = None,
collate_fn: Callable = None,
writer: Writer = None,
transform: "torchrl.envs.Transform" = None,
):
assert dataset_id in DATASET_IDS
super().__init__(
dataset_id,
version,
batch_size,
shuffle=shuffle,
root=root,
@@ -115,11 +113,11 @@ class AlohaDataset(AbstractDataset):
@property
def stats_patterns(self) -> dict:
d = {
("observation", "state"): "b c -> c",
("action",): "b c -> c",
("observation", "state"): "b c -> 1 c",
("action",): "b c -> 1 c",
}
for cam in CAMERAS[self.dataset_id]:
d[("observation", "image", cam)] = "b c h w -> c 1 1"
d[("observation", "image", cam)] = "b c h w -> 1 c 1 1"
return d
@property

58
lerobot/common/datasets/factory.py
View File

@@ -5,7 +5,7 @@ from pathlib import Path
import torch
from torchrl.data.replay_buffers import PrioritizedSliceSampler, SliceSampler
from lerobot.common.transforms import NormalizeTransform, Prod
from lerobot.common.envs.transforms import NormalizeTransform, Prod
# DATA_DIR specifies to location where datasets are loaded. By default, DATA_DIR is None and
# we load from `$HOME/.cache/huggingface/hub/datasets`. For our unit tests, we set `DATA_DIR=tests/data`
@@ -14,13 +14,7 @@ DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
def make_offline_buffer(
cfg,
overwrite_sampler=None,
# set normalize=False to remove all transformations and keep images unnormalized in [0,255]
normalize=True,
overwrite_batch_size=None,
overwrite_prefetch=None,
stats_path=None,
cfg, overwrite_sampler=None, normalize=True, overwrite_batch_size=None, overwrite_prefetch=None
):
if cfg.policy.balanced_sampling:
assert cfg.online_steps > 0
@@ -65,24 +59,27 @@ def make_offline_buffer(
sampler = overwrite_sampler
if cfg.env.name == "simxarm":
from lerobot.common.datasets.simxarm import SimxarmDataset
from lerobot.common.datasets.simxarm import SimxarmExperienceReplay
clsfunc = SimxarmDataset
clsfunc = SimxarmExperienceReplay
dataset_id = f"xarm_{cfg.env.task}_medium"
elif cfg.env.name == "pusht":
from lerobot.common.datasets.pusht import PushtDataset
from lerobot.common.datasets.pusht import PushtExperienceReplay
clsfunc = PushtDataset
clsfunc = PushtExperienceReplay
dataset_id = "pusht"
elif cfg.env.name == "aloha":
from lerobot.common.datasets.aloha import AlohaDataset
from lerobot.common.datasets.aloha import AlohaExperienceReplay
clsfunc = AlohaDataset
clsfunc = AlohaExperienceReplay
dataset_id = f"aloha_{cfg.env.task}"
else:
raise ValueError(cfg.env.name)
offline_buffer = clsfunc(
dataset_id=cfg.dataset_id,
dataset_id=dataset_id,
sampler=sampler,
batch_size=batch_size,
root=DATA_DIR,
@@ -98,15 +95,13 @@ def make_offline_buffer(
else:
img_keys = offline_buffer.image_keys
transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
if normalize:
transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
# TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max, min_max_from_spec
stats = offline_buffer.compute_or_load_stats()
# TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max,
# min_max_from_spec
stats = offline_buffer.compute_or_load_stats() if stats_path is None else torch.load(stats_path)
# we only normalize the state and action, since the images are usually normalized inside the model for
# now (except for tdmpc: see the following)
# we only normalize the state and action, since the images are usually normalized inside the model for now (except for tdmpc: see the following)
in_keys = [("observation", "state"), ("action")]
if cfg.policy.name == "tdmpc":
@@ -123,28 +118,11 @@ def make_offline_buffer(
stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
# if (
# cfg.env.name == "aloha"
# and cfg.env.task == "sim_transfer_cube"
# and cfg.policy.name == "act"
# ):
# import pickle
# with open(
# "/home/rcadene/code/act/tmp/2024_03_10_sim_transfer_cube_scripted/dataset_stats.pkl", "rb"
# ) as file:
# dataset_stats = pickle.load(file)
# stats["action", "mean"] = torch.from_numpy(dataset_stats["action_mean"])
# stats["action", "std"] = torch.from_numpy(dataset_stats["action_std"])
# stats["observation", "state", "mean"] = torch.from_numpy(dataset_stats["qpos_mean"])
# stats["observation", "state", "std"] = torch.from_numpy(dataset_stats["qpos_std"])
# TODO(rcadene): remove this and put it in config. Ideally we want to reproduce SOTA results just with mean_std
normalization_mode = "mean_std" if cfg.env.name == "aloha" else "min_max"
transforms.append(NormalizeTransform(stats, in_keys, mode=normalization_mode))
offline_buffer.set_transform(transforms)
offline_buffer.set_transform(transforms)
if not overwrite_sampler:
index = torch.arange(0, offline_buffer.num_samples, 1)

18
lerobot/common/datasets/pusht.py
View File

@@ -9,11 +9,11 @@ import torch
import torchrl
import tqdm
from tensordict import TensorDict
from torchrl.data.replay_buffers.samplers import Sampler
from torchrl.data.replay_buffers.samplers import SliceSampler
from torchrl.data.replay_buffers.storages import TensorStorage
from torchrl.data.replay_buffers.writers import Writer
from lerobot.common.datasets.abstract import AbstractDataset
from lerobot.common.datasets.abstract import AbstractExperienceReplay
from lerobot.common.datasets.utils import download_and_extract_zip
from lerobot.common.envs.pusht.pusht_env import pymunk_to_shapely
from lerobot.common.policies.diffusion.replay_buffer import ReplayBuffer as DiffusionPolicyReplayBuffer
@@ -83,27 +83,23 @@ def add_tee(
return body
class PushtDataset(AbstractDataset):
available_datasets = ["pusht"]
class PushtExperienceReplay(AbstractExperienceReplay):
def __init__(
self,
dataset_id: str,
version: str | None = "v1.2",
batch_size: int | None = None,
batch_size: int = None,
*,
shuffle: bool = True,
root: Path | None = None,
pin_memory: bool = False,
prefetch: int = None,
sampler: Sampler | None = None,
collate_fn: Callable | None = None,
writer: Writer | None = None,
sampler: SliceSampler = None,
collate_fn: Callable = None,
writer: Writer = None,
transform: "torchrl.envs.Transform" = None,
):
super().__init__(
dataset_id,
version,
batch_size,
shuffle=shuffle,
root=root,

32
lerobot/common/datasets/simxarm.py
View File

@@ -8,12 +8,12 @@ import torchrl
import tqdm
from tensordict import TensorDict
from torchrl.data.replay_buffers.samplers import (
Sampler,
SliceSampler,
)
from torchrl.data.replay_buffers.storages import TensorStorage
from torchrl.data.replay_buffers.writers import Writer
from lerobot.common.datasets.abstract import AbstractDataset
from lerobot.common.datasets.abstract import AbstractExperienceReplay
def download():
@@ -32,7 +32,7 @@ def download():
Path(download_path).unlink()
class SimxarmDataset(AbstractDataset):
class SimxarmExperienceReplay(AbstractExperienceReplay):
available_datasets = [
"xarm_lift_medium",
]
@@ -40,21 +40,19 @@ class SimxarmDataset(AbstractDataset):
def __init__(
self,
dataset_id: str,
version: str | None = "v1.1",
batch_size: int | None = None,
batch_size: int = None,
*,
shuffle: bool = True,
root: Path | None = None,
pin_memory: bool = False,
prefetch: int = None,
sampler: Sampler | None = None,
collate_fn: Callable | None = None,
writer: Writer | None = None,
sampler: SliceSampler = None,
collate_fn: Callable = None,
writer: Writer = None,
transform: "torchrl.envs.Transform" = None,
):
super().__init__(
dataset_id,
version,
batch_size,
shuffle=shuffle,
root=root,
@@ -67,11 +65,11 @@ class SimxarmDataset(AbstractDataset):
)
def _download_and_preproc_obsolete(self):
# assert self.root is not None
assert self.root is not None
# TODO(rcadene): finish download
# download()
download()
dataset_path = self.root / f"{self.dataset_id}" / "buffer.pkl"
dataset_path = self.root / f"{self.dataset_id}_raw" / "buffer.pkl"
print(f"Using offline dataset '{dataset_path}'")
with open(dataset_path, "rb") as f:
dataset_dict = pickle.load(f)
@@ -105,19 +103,15 @@ class SimxarmDataset(AbstractDataset):
"frame_id": torch.arange(0, num_frames, 1),
("next", "observation", "image"): next_image,
("next", "observation", "state"): next_state,
("next", "reward"): next_reward,
("next", "done"): next_done,
("next", "observation", "reward"): next_reward,
("next", "observation", "done"): next_done,
},
batch_size=num_frames,
)
if episode_id == 0:
# hack to initialize tensordict data structure to store episodes
td_data = (
episode[0]
.expand(total_frames)
.memmap_like(self.root / f"{self.dataset_id}" / "replay_buffer")
)
td_data = episode[0].expand(total_frames).memmap_like(self.root / f"{self.dataset_id}")
td_data[idx0:idx1] = episode

62
lerobot/common/envs/abstract.py
View File

@@ -1,27 +1,12 @@
import abc
from collections import deque
from typing import Optional
from tensordict import TensorDict
from torchrl.envs import EnvBase
from lerobot.common.utils import set_global_seed
class AbstractEnv(EnvBase):
"""
Note:
When implementing a concrete class (e.g. `AlohaDataset`, `PushtEnv`, `DiffusionPolicy`), you need to:
1. set the required class attributes:
- for classes inheriting from `AbstractDataset`: `available_datasets`
- for classes inheriting from `AbstractEnv`: `name`, `available_tasks`
- for classes inheriting from `AbstractPolicy`: `name`
2. update variables in `lerobot/__init__.py` (e.g. `available_envs`, `available_datasets_per_envs`, `available_policies`)
3. update variables in `tests/test_available.py` by importing your new class
"""
name: str | None = None # same name should be used to instantiate the environment in factory.py
available_tasks: list[str] | None = None # for instance: sim_insertion, sim_transfer_cube, pusht, lift
def __init__(
self,
task,
@@ -35,14 +20,6 @@ class AbstractEnv(EnvBase):
num_prev_action=0,
):
super().__init__(device=device, batch_size=[])
assert self.name is not None, "Subclasses of `AbstractEnv` should set the `name` class attribute."
assert (
self.available_tasks is not None
), "Subclasses of `AbstractEnv` should set the `available_tasks` class attribute."
assert (
task in self.available_tasks
), f"The provided task ({task}) is not on the list of available tasks {self.available_tasks}."
self.task = task
self.frame_skip = frame_skip
self.from_pixels = from_pixels
@@ -50,6 +27,7 @@ class AbstractEnv(EnvBase):
self.image_size = image_size
self.num_prev_obs = num_prev_obs
self.num_prev_action = num_prev_action
self._rendering_hooks = []
if pixels_only:
assert from_pixels
@@ -58,13 +36,7 @@ class AbstractEnv(EnvBase):
self._make_env()
self._make_spec()
# self._next_seed will be used for the next reset. It is recommended that when self.set_seed is called
# you store the return value in self._next_seed (it will be a new randomly generated seed).
self._next_seed = seed
# Don't store the result of this in self._next_seed, as we want to make sure that the first time
# self._reset is called, we use seed.
self.set_seed(seed)
self._current_seed = self.set_seed(seed)
if self.num_prev_obs > 0:
self._prev_obs_image_queue = deque(maxlen=self.num_prev_obs)
@@ -73,20 +45,36 @@ class AbstractEnv(EnvBase):
raise NotImplementedError()
# self._prev_action_queue = deque(maxlen=self.num_prev_action)
def register_rendering_hook(self, func):
self._rendering_hooks.append(func)
def call_rendering_hooks(self):
for func in self._rendering_hooks:
func(self)
def reset_rendering_hooks(self):
self._rendering_hooks = []
@abc.abstractmethod
def render(self, mode="rgb_array", width=640, height=480):
raise NotImplementedError("Abstract method")
raise NotImplementedError()
@abc.abstractmethod
def _reset(self, tensordict: Optional[TensorDict] = None):
raise NotImplementedError("Abstract method")
raise NotImplementedError()
@abc.abstractmethod
def _step(self, tensordict: TensorDict):
raise NotImplementedError("Abstract method")
raise NotImplementedError()
@abc.abstractmethod
def _make_env(self):
raise NotImplementedError("Abstract method")
raise NotImplementedError()
@abc.abstractmethod
def _make_spec(self):
raise NotImplementedError("Abstract method")
raise NotImplementedError()
@abc.abstractmethod
def _set_seed(self, seed: Optional[int]):
set_global_seed(seed)
raise NotImplementedError()

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129
lerobot/common/envs/aloha/env.py
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@@ -29,16 +29,12 @@ from lerobot.common.envs.aloha.tasks.sim_end_effector import (
TransferCubeEndEffectorTask,
)
from lerobot.common.envs.aloha.utils import sample_box_pose, sample_insertion_pose
from lerobot.common.utils import set_global_seed
from lerobot.common.utils import set_seed
_has_gym = importlib.util.find_spec("gymnasium") is not None
_has_gym = importlib.util.find_spec("gym") is not None
class AlohaEnv(AbstractEnv):
name = "aloha"
available_tasks = ["sim_insertion", "sim_transfer_cube"]
_reset_warning_issued = False
def __init__(
self,
task,
@@ -65,7 +61,7 @@ class AlohaEnv(AbstractEnv):
def _make_env(self):
if not _has_gym:
raise ImportError("Cannot import gymnasium.")
raise ImportError("Cannot import gym.")
if not self.from_pixels:
raise NotImplementedError()
@@ -124,74 +120,91 @@ class AlohaEnv(AbstractEnv):
return obs
def _reset(self, tensordict: Optional[TensorDict] = None):
if tensordict is not None and not AlohaEnv._reset_warning_issued:
logging.warning(f"{self.__class__.__name__}._reset ignores the provided tensordict.")
AlohaEnv._reset_warning_issued = True
td = tensordict
if td is None or td.is_empty():
# we need to handle seed iteration, since self._env.reset() rely an internal _seed.
self._current_seed += 1
self.set_seed(self._current_seed)
# Seed the environment and update the seed to be used for the next reset.
self._next_seed = self.set_seed(self._next_seed)
# TODO(rcadene): do not use global variable for this
if "sim_transfer_cube" in self.task:
BOX_POSE[0] = sample_box_pose() # used in sim reset
elif "sim_insertion" in self.task:
BOX_POSE[0] = np.concatenate(sample_insertion_pose()) # used in sim reset
# TODO(rcadene): do not use global variable for this
if "sim_transfer_cube" in self.task:
BOX_POSE[0] = sample_box_pose() # used in sim reset
elif "sim_insertion" in self.task:
BOX_POSE[0] = np.concatenate(sample_insertion_pose()) # used in sim reset
raw_obs = self._env.reset()
# TODO(rcadene): add assert
# assert self._current_seed == self._env._seed
raw_obs = self._env.reset()
obs = self._format_raw_obs(raw_obs.observation)
obs = self._format_raw_obs(raw_obs.observation)
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue = deque(
[obs["image"]["top"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
if "state" in obs:
self._prev_obs_state_queue = deque(
[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue = deque(
[obs["image"]["top"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
if "state" in obs:
self._prev_obs_state_queue = deque(
[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"done": torch.tensor([False], dtype=torch.bool),
},
batch_size=[],
)
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"done": torch.tensor([False], dtype=torch.bool),
},
batch_size=[],
)
else:
raise NotImplementedError()
self.call_rendering_hooks()
return td
def _step(self, tensordict: TensorDict):
td = tensordict
action = td["action"].numpy()
assert action.ndim == 1
# step expects shape=(4,) so we pad if necessary
# TODO(rcadene): add info["is_success"] and info["success"] ?
sum_reward = 0
_, reward, _, raw_obs = self._env.step(action)
if action.ndim == 1:
action = einops.repeat(action, "c -> t c", t=self.frame_skip)
else:
if self.frame_skip > 1:
raise NotImplementedError()
# TODO(rcadene): add an enum
success = done = reward == 4
obs = self._format_raw_obs(raw_obs)
num_action_steps = action.shape[0]
for i in range(num_action_steps):
_, reward, discount, raw_obs = self._env.step(action[i])
del discount # not used
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue.append(obs["image"]["top"])
stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
if "state" in obs:
self._prev_obs_state_queue.append(obs["state"])
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
# TOOD(rcadene): add an enum
success = done = reward == 4
sum_reward += reward
obs = self._format_raw_obs(raw_obs)
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue.append(obs["image"]["top"])
stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
if "state" in obs:
self._prev_obs_state_queue.append(obs["state"])
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
self.call_rendering_hooks()
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"reward": torch.tensor([reward], dtype=torch.float32),
# success and done are true when coverage > self.success_threshold in env
"reward": torch.tensor([sum_reward], dtype=torch.float32),
# succes and done are true when coverage > self.success_threshold in env
"done": torch.tensor([done], dtype=torch.bool),
"success": torch.tensor([success], dtype=torch.bool),
},
@@ -206,7 +219,7 @@ class AlohaEnv(AbstractEnv):
if self.from_pixels:
if isinstance(self.image_size, int):
image_shape = (3, self.image_size, self.image_size)
elif OmegaConf.is_list(self.image_size) or isinstance(self.image_size, list):
elif OmegaConf.is_list(self.image_size):
assert len(self.image_size) == 3 # c h w
assert self.image_size[0] == 3 # c is RGB
image_shape = tuple(self.image_size)
@@ -292,7 +305,7 @@ class AlohaEnv(AbstractEnv):
)
def _set_seed(self, seed: Optional[int]):
set_global_seed(seed)
set_seed(seed)
# TODO(rcadene): seed the env
# self._env.seed(seed)
logging.warning("Aloha env is not seeded")

69
lerobot/common/envs/factory.py
View File

@@ -1,23 +1,19 @@
from torchrl.envs import SerialEnv
from torchrl.envs.transforms import Compose, StepCounter, Transform, TransformedEnv
def make_env(cfg, transform=None):
"""
Note: The returned environment is wrapped in a torchrl.SerialEnv with cfg.rollout_batch_size underlying
environments. The env therefore returns batches.`
"""
kwargs = {
"frame_skip": cfg.env.action_repeat,
"from_pixels": cfg.env.from_pixels,
"pixels_only": cfg.env.pixels_only,
"image_size": cfg.env.image_size,
# TODO(rcadene): do we want a specific eval_env_seed?
"seed": cfg.seed,
"num_prev_obs": cfg.n_obs_steps - 1,
}
if cfg.env.name == "simxarm":
from lerobot.common.envs.simxarm.env import SimxarmEnv
from lerobot.common.envs.simxarm import SimxarmEnv
kwargs["task"] = cfg.env.task
clsfunc = SimxarmEnv
@@ -35,30 +31,43 @@ def make_env(cfg, transform=None):
else:
raise ValueError(cfg.env.name)
def _make_env(seed):
nonlocal kwargs
kwargs["seed"] = seed
env = clsfunc(**kwargs)
env = clsfunc(**kwargs)
# limit rollout to max_steps
env = TransformedEnv(env, StepCounter(max_steps=cfg.env.episode_length))
# limit rollout to max_steps
env = TransformedEnv(env, StepCounter(max_steps=cfg.env.episode_length))
if transform is not None:
# useful to add normalization
if isinstance(transform, Compose):
for tf in transform:
env.append_transform(tf.clone())
elif isinstance(transform, Transform):
env.append_transform(transform.clone())
else:
raise NotImplementedError()
if transform is not None:
# useful to add normalization
if isinstance(transform, Compose):
for tf in transform:
env.append_transform(tf.clone())
elif isinstance(transform, Transform):
env.append_transform(transform.clone())
else:
raise NotImplementedError()
return env
return env
return SerialEnv(
cfg.rollout_batch_size,
create_env_fn=_make_env,
create_env_kwargs=[
{"seed": env_seed} for env_seed in range(cfg.seed, cfg.seed + cfg.rollout_batch_size)
],
)
# def make_env(env_name, frame_skip, device, is_test=False):
# env = GymEnv(
# env_name,
# frame_skip=frame_skip,
# from_pixels=True,
# pixels_only=False,
# device=device,
# )
# env = TransformedEnv(env)
# env.append_transform(NoopResetEnv(noops=30, random=True))
# if not is_test:
# env.append_transform(EndOfLifeTransform())
# env.append_transform(RewardClipping(-1, 1))
# env.append_transform(ToTensorImage())
# env.append_transform(GrayScale())
# env.append_transform(Resize(84, 84))
# env.append_transform(CatFrames(N=4, dim=-3))
# env.append_transform(RewardSum())
# env.append_transform(StepCounter(max_steps=4500))
# env.append_transform(DoubleToFloat())
# env.append_transform(VecNorm(in_keys=["pixels"]))
# return env

141
lerobot/common/envs/pusht/env.py
View File

@@ -1,10 +1,8 @@
import importlib
import logging
from collections import deque
from typing import Optional
import cv2
import numpy as np
import einops
import torch
from tensordict import TensorDict
from torchrl.data.tensor_specs import (
@@ -16,16 +14,12 @@ from torchrl.data.tensor_specs import (
from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform
from lerobot.common.envs.abstract import AbstractEnv
from lerobot.common.utils import set_global_seed
from lerobot.common.utils import set_seed
_has_gym = importlib.util.find_spec("gymnasium") is not None
_has_gym = importlib.util.find_spec("gym") is not None
class PushtEnv(AbstractEnv):
name = "pusht"
available_tasks = ["pusht"]
_reset_warning_issued = False
def __init__(
self,
task="pusht",
@@ -52,7 +46,7 @@ class PushtEnv(AbstractEnv):
def _make_env(self):
if not _has_gym:
raise ImportError("Cannot import gymnasium.")
raise ImportError("Cannot import gym.")
# TODO(rcadene) (PushTEnv is similar to PushTImageEnv, but without the image rendering, it's faster to iterate on)
# from lerobot.common.envs.pusht.pusht_env import PushTEnv
@@ -63,30 +57,12 @@ class PushtEnv(AbstractEnv):
self._env = PushTImageEnv(render_size=self.image_size)
def render(self, mode="rgb_array", width=96, height=96, with_marker=True):
"""
with_marker adds a cursor showing the targeted action for the controller.
"""
def render(self, mode="rgb_array", width=384, height=384):
if width != height:
raise NotImplementedError()
tmp = self._env.render_size
if width != self._env.render_size:
self._env.render_cache = None
self._env.render_size = width
out = self._env.render(mode).copy()
if with_marker and self._env.latest_action is not None:
action = np.array(self._env.latest_action)
coord = (action / 512 * self._env.render_size).astype(np.int32)
marker_size = int(8 / 96 * self._env.render_size)
thickness = int(1 / 96 * self._env.render_size)
cv2.drawMarker(
out,
coord,
color=(255, 0, 0),
markerType=cv2.MARKER_CROSS,
markerSize=marker_size,
thickness=thickness,
)
self._env.render_size = width
out = self._env.render(mode)
self._env.render_size = tmp
return out
@@ -104,65 +80,80 @@ class PushtEnv(AbstractEnv):
return obs
def _reset(self, tensordict: Optional[TensorDict] = None):
if tensordict is not None and not PushtEnv._reset_warning_issued:
logging.warning(f"{self.__class__.__name__}._reset ignores the provided tensordict.")
PushtEnv._reset_warning_issued = True
td = tensordict
if td is None or td.is_empty():
# we need to handle seed iteration, since self._env.reset() rely an internal _seed.
self._current_seed += 1
self.set_seed(self._current_seed)
raw_obs = self._env.reset()
assert self._current_seed == self._env._seed
# Seed the environment and update the seed to be used for the next reset.
self._next_seed = self.set_seed(self._next_seed)
raw_obs = self._env.reset()
obs = self._format_raw_obs(raw_obs)
obs = self._format_raw_obs(raw_obs)
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue = deque(
[obs["image"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
if "state" in obs:
self._prev_obs_state_queue = deque(
[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue = deque(
[obs["image"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
if "state" in obs:
self._prev_obs_state_queue = deque(
[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"done": torch.tensor([False], dtype=torch.bool),
},
batch_size=[],
)
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"done": torch.tensor([False], dtype=torch.bool),
},
batch_size=[],
)
else:
raise NotImplementedError()
self.call_rendering_hooks()
return td
def _step(self, tensordict: TensorDict):
td = tensordict
action = td["action"].numpy()
assert action.ndim == 1
# step expects shape=(4,) so we pad if necessary
# TODO(rcadene): add info["is_success"] and info["success"] ?
sum_reward = 0
raw_obs, reward, done, info = self._env.step(action)
if action.ndim == 1:
action = einops.repeat(action, "c -> t c", t=self.frame_skip)
else:
if self.frame_skip > 1:
raise NotImplementedError()
obs = self._format_raw_obs(raw_obs)
num_action_steps = action.shape[0]
for i in range(num_action_steps):
raw_obs, reward, done, info = self._env.step(action[i])
sum_reward += reward
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue.append(obs["image"])
stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
if "state" in obs:
self._prev_obs_state_queue.append(obs["state"])
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
obs = self._format_raw_obs(raw_obs)
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue.append(obs["image"])
stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
if "state" in obs:
self._prev_obs_state_queue.append(obs["state"])
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
self.call_rendering_hooks()
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"reward": torch.tensor([reward], dtype=torch.float32),
# success and done are true when coverage > self.success_threshold in env
"reward": torch.tensor([sum_reward], dtype=torch.float32),
# succes and done are true when coverage > self.success_threshold in env
"done": torch.tensor([done], dtype=torch.bool),
"success": torch.tensor([done], dtype=torch.bool),
},
@@ -239,7 +230,5 @@ class PushtEnv(AbstractEnv):
)
def _set_seed(self, seed: Optional[int]):
# Set global seed.
set_global_seed(seed)
# Set PushTImageEnv seed as it relies on it's own internal _seed attribute.
set_seed(seed)
self._env.seed(seed)

6
lerobot/common/envs/pusht/pusht_env.py
View File

@@ -1,14 +1,14 @@
import collections
import cv2
import gymnasium as gym
import gym
import numpy as np
import pygame
import pymunk
import pymunk.pygame_util
import shapely.geometry as sg
import skimage.transform as st
from gymnasium import spaces
from gym import spaces
from pymunk.vec2d import Vec2d
from lerobot.common.envs.pusht.pymunk_override import DrawOptions
@@ -33,7 +33,7 @@ class PushTEnv(gym.Env):
def __init__(
self,
legacy=True, # compatibility with original
legacy=False,
block_cog=None,
damping=None,
render_action=True,

20
lerobot/common/envs/pusht/pusht_image_env.py
View File

@@ -1,5 +1,6 @@
import cv2
import numpy as np
from gymnasium import spaces
from gym import spaces
from lerobot.common.envs.pusht.pusht_env import PushTEnv
@@ -7,8 +8,7 @@ from lerobot.common.envs.pusht.pusht_env import PushTEnv
class PushTImageEnv(PushTEnv):
metadata = {"render.modes": ["rgb_array"], "video.frames_per_second": 10}
# Note: legacy defaults to True for compatibility with original
def __init__(self, legacy=True, block_cog=None, damping=None, render_size=96):
def __init__(self, legacy=False, block_cog=None, damping=None, render_size=96):
super().__init__(
legacy=legacy, block_cog=block_cog, damping=damping, render_size=render_size, render_action=False
)
@@ -28,6 +28,20 @@ class PushTImageEnv(PushTEnv):
img_obs = np.moveaxis(img, -1, 0)
obs = {"image": img_obs, "agent_pos": agent_pos}
# draw action
if self.latest_action is not None:
action = np.array(self.latest_action)
coord = (action / 512 * 96).astype(np.int32)
marker_size = int(8 / 96 * self.render_size)
thickness = int(1 / 96 * self.render_size)
cv2.drawMarker(
img,
coord,
color=(255, 0, 0),
markerType=cv2.MARKER_CROSS,
markerSize=marker_size,
thickness=thickness,
)
self.render_cache = img
return obs

31
lerobot/common/envs/simxarm/env.py → lerobot/common/envs/simxarm.py
View File

@@ -1,5 +1,4 @@
import importlib
import logging
from collections import deque
from typing import Optional
@@ -16,17 +15,15 @@ from torchrl.data.tensor_specs import (
from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform
from lerobot.common.envs.abstract import AbstractEnv
from lerobot.common.utils import set_global_seed
from lerobot.common.utils import set_seed
MAX_NUM_ACTIONS = 4
_has_gym = importlib.util.find_spec("gymnasium") is not None
_has_gym = importlib.util.find_spec("gym") is not None
_has_simxarm = importlib.util.find_spec("simxarm") is not None and _has_gym
class SimxarmEnv(AbstractEnv):
name = "simxarm"
available_tasks = ["lift"]
def __init__(
self,
task,
@@ -52,12 +49,13 @@ class SimxarmEnv(AbstractEnv):
)
def _make_env(self):
if not _has_simxarm:
raise ImportError("Cannot import simxarm.")
if not _has_gym:
raise ImportError("Cannot import gymnasium.")
raise ImportError("Cannot import gym.")
import gymnasium
from lerobot.common.envs.simxarm.simxarm import TASKS
import gym
from simxarm import TASKS
if self.task not in TASKS:
raise ValueError(f"Unknown task {self.task}. Must be one of {list(TASKS.keys())}")
@@ -65,7 +63,7 @@ class SimxarmEnv(AbstractEnv):
self._env = TASKS[self.task]["env"]()
num_actions = len(TASKS[self.task]["action_space"])
self._action_space = gymnasium.spaces.Box(low=-1.0, high=1.0, shape=(num_actions,))
self._action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(num_actions,))
self._action_padding = np.zeros((MAX_NUM_ACTIONS - num_actions), dtype=np.float32)
if "w" not in TASKS[self.task]["action_space"]:
self._action_padding[-1] = 1.0
@@ -86,7 +84,7 @@ class SimxarmEnv(AbstractEnv):
else:
obs = {"state": torch.tensor(raw_obs["observation"], dtype=torch.float32)}
# obs = TensorDict(obs, batch_size=[])
obs = TensorDict(obs, batch_size=[])
return obs
def _reset(self, tensordict: Optional[TensorDict] = None):
@@ -120,6 +118,7 @@ class SimxarmEnv(AbstractEnv):
else:
raise NotImplementedError()
self.call_rendering_hooks()
return td
def _step(self, tensordict: TensorDict):
@@ -153,6 +152,8 @@ class SimxarmEnv(AbstractEnv):
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
self.call_rendering_hooks()
td = TensorDict(
{
"observation": self._format_raw_obs(raw_obs),
@@ -231,7 +232,5 @@ class SimxarmEnv(AbstractEnv):
)
def _set_seed(self, seed: Optional[int]):
set_global_seed(seed)
self._seed = seed
# TODO(aliberts): change self._reset so that it takes in a seed value
logging.warning("simxarm env is not properly seeded")
set_seed(seed)
self._env.seed(seed)

166
lerobot/common/envs/simxarm/simxarm/__init__.py
View File

@@ -1,166 +0,0 @@
from collections import OrderedDict, deque
import gymnasium as gym
import numpy as np
from gymnasium.wrappers import TimeLimit
from lerobot.common.envs.simxarm.simxarm.tasks.base import Base as Base
from lerobot.common.envs.simxarm.simxarm.tasks.lift import Lift
from lerobot.common.envs.simxarm.simxarm.tasks.peg_in_box import PegInBox
from lerobot.common.envs.simxarm.simxarm.tasks.push import Push
from lerobot.common.envs.simxarm.simxarm.tasks.reach import Reach
TASKS = OrderedDict(
(
(
"reach",
{
"env": Reach,
"action_space": "xyz",
"episode_length": 50,
"description": "Reach a target location with the end effector",
},
),
(
"push",
{
"env": Push,
"action_space": "xyz",
"episode_length": 50,
"description": "Push a cube to a target location",
},
),
(
"peg_in_box",
{
"env": PegInBox,
"action_space": "xyz",
"episode_length": 50,
"description": "Insert a peg into a box",
},
),
(
"lift",
{
"env": Lift,
"action_space": "xyzw",
"episode_length": 50,
"description": "Lift a cube above a height threshold",
},
),
)
)
class SimXarmWrapper(gym.Wrapper):
"""
A wrapper for the SimXarm environments. This wrapper is used to
convert the action and observation spaces to the correct format.
"""
def __init__(self, env, task, obs_mode, image_size, action_repeat, frame_stack=1, channel_last=False):
super().__init__(env)
self._env = env
self.obs_mode = obs_mode
self.image_size = image_size
self.action_repeat = action_repeat
self.frame_stack = frame_stack
self._frames = deque([], maxlen=frame_stack)
self.channel_last = channel_last
self._max_episode_steps = task["episode_length"] // action_repeat
image_shape = (
(image_size, image_size, 3 * frame_stack)
if channel_last
else (3 * frame_stack, image_size, image_size)
)
if obs_mode == "state":
self.observation_space = env.observation_space["observation"]
elif obs_mode == "rgb":
self.observation_space = gym.spaces.Box(low=0, high=255, shape=image_shape, dtype=np.uint8)
elif obs_mode == "all":
self.observation_space = gym.spaces.Dict(
state=gym.spaces.Box(low=-np.inf, high=np.inf, shape=(4,), dtype=np.float32),
rgb=gym.spaces.Box(low=0, high=255, shape=image_shape, dtype=np.uint8),
)
else:
raise ValueError(f"Unknown obs_mode {obs_mode}. Must be one of [rgb, all, state]")
self.action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(len(task["action_space"]),))
self.action_padding = np.zeros(4 - len(task["action_space"]), dtype=np.float32)
if "w" not in task["action_space"]:
self.action_padding[-1] = 1.0
def _render_obs(self):
obs = self.render(mode="rgb_array", width=self.image_size, height=self.image_size)
if not self.channel_last:
obs = obs.transpose(2, 0, 1)
return obs.copy()
def _update_frames(self, reset=False):
pixels = self._render_obs()
self._frames.append(pixels)
if reset:
for _ in range(1, self.frame_stack):
self._frames.append(pixels)
assert len(self._frames) == self.frame_stack
def transform_obs(self, obs, reset=False):
if self.obs_mode == "state":
return obs["observation"]
elif self.obs_mode == "rgb":
self._update_frames(reset=reset)
rgb_obs = np.concatenate(list(self._frames), axis=-1 if self.channel_last else 0)
return rgb_obs
elif self.obs_mode == "all":
self._update_frames(reset=reset)
rgb_obs = np.concatenate(list(self._frames), axis=-1 if self.channel_last else 0)
return OrderedDict((("rgb", rgb_obs), ("state", self.robot_state)))
else:
raise ValueError(f"Unknown obs_mode {self.obs_mode}. Must be one of [rgb, all, state]")
def reset(self):
return self.transform_obs(self._env.reset(), reset=True)
def step(self, action):
action = np.concatenate([action, self.action_padding])
reward = 0.0
for _ in range(self.action_repeat):
obs, r, done, info = self._env.step(action)
reward += r
return self.transform_obs(obs), reward, done, info
def render(self, mode="rgb_array", width=384, height=384, **kwargs):
return self._env.render(mode, width=width, height=height)
@property
def state(self):
return self._env.robot_state
def make(task, obs_mode="state", image_size=84, action_repeat=1, frame_stack=1, channel_last=False, seed=0):
"""
Create a new environment.
Args:
task (str): The task to create an environment for. Must be one of:
- 'reach'
- 'push'
- 'peg-in-box'
- 'lift'
obs_mode (str): The observation mode to use. Must be one of:
- 'state': Only state observations
- 'rgb': RGB images
- 'all': RGB images and state observations
image_size (int): The size of the image observations
action_repeat (int): The number of times to repeat the action
seed (int): The random seed to use
Returns:
gym.Env: The environment
"""
if task not in TASKS:
raise ValueError(f"Unknown task {task}. Must be one of {list(TASKS.keys())}")
env = TASKS[task]["env"]()
env = TimeLimit(env, TASKS[task]["episode_length"])
env = SimXarmWrapper(env, TASKS[task], obs_mode, image_size, action_repeat, frame_stack, channel_last)
env.seed(seed)
return env

0
lerobot/common/envs/simxarm/simxarm/tasks/__init__.py
View File

53
lerobot/common/envs/simxarm/simxarm/tasks/assets/lift.xml
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@@ -1,53 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<mujoco>
<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
<size nconmax="2000" njmax="500"/>
<option timestep="0.002">
<flag warmstart="enable"></flag>
</option>
<include file="shared.xml"></include>
<worldbody>
<body name="floor0" pos="0 0 0">
<geom name="floorgeom0" pos="1.2 -2.0 0" size="20.0 20.0 1" type="plane" condim="3" material="floor_mat"></geom>
</body>
<include file="xarm.xml"></include>
<body pos="0.75 0 0.6325" name="pedestal0">
<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
</body>
<body pos="1.5 0.075 0.3425" name="table0">
<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 1 1"></geom>
</body>
<body name="object" pos="1.405 0.3 0.58625">
<joint name="object_joint0" type="free" limited="false"></joint>
<geom size="0.035 0.035 0.035" type="box" name="object0" material="block_mat" density="50000" condim="4" friction="1 1 1" solimp="1 1 1" solref="0.02 1"></geom>
<site name="object_site" pos="0 0 0" size="0.035 0.035 0.035" rgba="1 0 0 0" type="box"></site>
</body>
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
<light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true" diffuse="0.3 0.3 0.3" castshadow="false" name="light3"></light>
<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
</worldbody>
<equality>
<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
</equality>
<actuator>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
</actuator>
</mujoco>

3
lerobot/common/envs/simxarm/simxarm/tasks/assets/mesh/base_link.stl
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lerobot/common/envs/simxarm/simxarm/tasks/assets/peg_in_box.xml
View File

@@ -1,74 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<mujoco>
<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
<size nconmax="2000" njmax="500"/>
<option timestep="0.001">
<flag warmstart="enable"></flag>
</option>
<include file="shared.xml"></include>
<worldbody>
<body name="floor0" pos="0 0 0">
<geom name="floorgeom0" pos="1.2 -2.0 0" size="1.0 10.0 1" type="plane" condim="3" material="floor_mat"></geom>
</body>
<include file="xarm.xml"></include>
<body pos="0.75 0 0.6325" name="pedestal0">
<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
</body>
<body pos="1.5 0.075 0.3425" name="table0">
<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 0.005 0.0002"></geom>
</body>
<body name="box0" pos="1.605 0.25 0.55">
<joint name="box_joint0" type="free" limited="false"></joint>
<site name="box_site" pos="0 0.075 -0.01" size="0.02" rgba="0 0 0 0" type="sphere"></site>
<geom name="box_side0" pos="0 0 0" size="0.065 0.002 0.04" type= "box" rgba="0.8 0.1 0.1 1" mass ="1" condim="4" />
<geom name="box_side1" pos="0 0.149 0" size="0.065 0.002 0.04" type="box" rgba="0.9 0.2 0.2 1" mass ="2" condim="4" />
<geom name="box_side2" pos="0.064 0.074 0" size="0.002 0.075 0.04" type="box" rgba="0.8 0.1 0.1 1" mass ="2" condim="4" />
<geom name="box_side3" pos="-0.064 0.074 0" size="0.002 0.075 0.04" type="box" rgba="0.9 0.2 0.2 1" mass ="2" condim="4" />
<geom name="box_side4" pos="-0 0.074 -0.038" size="0.065 0.075 0.002" type="box" rgba="0.5 0 0 1" mass ="2" condim="4"/>
</body>
<body name="object0" pos="1.4 0.25 0.65">
<joint name="object_joint0" type="free" limited="false"></joint>
<geom name="object_target0" type="cylinder" pos="0 0 -0.05" size="0.03 0.035" rgba="0.6 0.8 0.5 1" mass ="0.1" condim="3" />
<site name="object_site" pos="0 0 -0.05" size="0.0325 0.0375" rgba="0 0 0 0" type="cylinder"></site>
<body name="B0" pos="0 0 0" euler="0 0 0 ">
<joint name="B0:joint" type="slide" limited="true" axis="0 0 1" damping="0.05" range="0.0001 0.0001001" solimpfriction="0.98 0.98 0.95" frictionloss="1"></joint>
<geom type="capsule" size="0.002 0.03" rgba="0 0 0 1" mass="0.001" condim="4"/>
<body name="B1" pos="0 0 0.04" euler="0 3.14 0 ">
<joint name="B1:joint1" type="hinge" axis="1 0 0" range="-0.1 0.1" frictionloss="1"></joint>
<joint name="B1:joint2" type="hinge" axis="0 1 0" range="-0.1 0.1" frictionloss="1"></joint>
<joint name="B1:joint3" type="hinge" axis="0 0 1" range="-0.1 0.1" frictionloss="1"></joint>
<geom type="capsule" size="0.002 0.004" rgba="1 0 0 0" mass="0.001" condim="4"/>
</body>
</body>
</body>
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
<light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true" diffuse="0.3 0.3 0.3" castshadow="false" name="light3"></light>
<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
</worldbody>
<equality>
<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<weld body1="right_hand" body2="B1" solimp="0.99 0.99 0.99" solref="0.02 1"></weld>
<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
</equality>
<actuator>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
</actuator>
</mujoco>

54
lerobot/common/envs/simxarm/simxarm/tasks/assets/push.xml
View File

@@ -1,54 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<mujoco>
<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
<size nconmax="2000" njmax="500"/>
<option timestep="0.002">
<flag warmstart="enable"></flag>
</option>
<include file="shared.xml"></include>
<worldbody>
<body name="floor0" pos="0 0 0">
<geom name="floorgeom0" pos="1.2 -2.0 0" size="1.0 10.0 1" type="plane" condim="3" material="floor_mat"></geom>
<site name="target0" pos="1.565 0.3 0.545" size="0.0475 0.001" rgba="1 0 0 1" type="cylinder"></site>
</body>
<include file="xarm.xml"></include>
<body pos="0.75 0 0.6325" name="pedestal0">
<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
</body>
<body pos="1.5 0.075 0.3425" name="table0">
<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 0.005 0.0002"></geom>
</body>
<body name="object" pos="1.655 0.3 0.68">
<joint name="object_joint0" type="free" limited="false"></joint>
<geom size="0.024 0.024 0.024" type="box" name="object" material="block_mat" density="50000" condim="4" friction="1 1 1" solimp="1 1 1" solref="0.02 1"></geom>
<site name="object_site" pos="0 0 0" size="0.024 0.024 0.024" rgba="0 0 0 0" type="box"></site>
</body>
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
<light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true" diffuse="0.3 0.3 0.3" castshadow="false" name="light3"></light>
<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
</worldbody>
<equality>
<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
</equality>
<actuator>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
</actuator>
</mujoco>

48
lerobot/common/envs/simxarm/simxarm/tasks/assets/reach.xml
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@@ -1,48 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<mujoco>
<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
<size nconmax="2000" njmax="500"/>
<option timestep="0.002">
<flag warmstart="enable"></flag>
</option>
<include file="shared.xml"></include>
<worldbody>
<body name="floor0" pos="0 0 0">
<geom name="floorgeom0" pos="1.2 -2.0 0" size="1.0 10.0 1" type="plane" condim="3" material="floor_mat"></geom>
<site name="target0" pos="1.605 0.3 0.58" size="0.0475 0.001" rgba="1 0 0 1" type="cylinder"></site>
</body>
<include file="xarm.xml"></include>
<body pos="0.75 0 0.6325" name="pedestal0">
<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
</body>
<body pos="1.5 0.075 0.3425" name="table0">
<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 0.005 0.0002"></geom>
</body>
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
<light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true" diffuse="0.3 0.3 0.3" castshadow="false" name="light3"></light>
<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
</worldbody>
<equality>
<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
</equality>
<actuator>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
</actuator>
</mujoco>

51
lerobot/common/envs/simxarm/simxarm/tasks/assets/shared.xml
View File

@@ -1,51 +0,0 @@
<mujoco>
<asset>
<texture type="skybox" builtin="gradient" rgb1="0.0 0.0 0.0" rgb2="0.0 0.0 0.0" width="32" height="32"></texture>
<material name="floor_mat" specular="0" shininess="0.0" reflectance="0" rgba="0.043 0.055 0.051 1"></material>
<material name="table_mat" specular="0.2" shininess="0.2" reflectance="0" rgba="1 1 1 1"></material>
<material name="pedestal_mat" specular="0.35" shininess="0.5" reflectance="0" rgba="0.705 0.585 0.405 1"></material>
<material name="block_mat" specular="0.5" shininess="0.9" reflectance="0.05" rgba="0.373 0.678 0.627 1"></material>
<material name="robot0:geomMat" shininess="0.03" specular="0.4"></material>
<material name="robot0:gripper_finger_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
<material name="robot0:gripper_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
<material name="background:gripper_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
<material name="robot0:arm_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
<material name="robot0:head_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
<material name="robot0:torso_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
<material name="robot0:base_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
<mesh name="link_base" file="link_base.stl" />
<mesh name="link1" file="link1.stl" />
<mesh name="link2" file="link2.stl" />
<mesh name="link3" file="link3.stl" />
<mesh name="link4" file="link4.stl" />
<mesh name="link5" file="link5.stl" />
<mesh name="link6" file="link6.stl" />
<mesh name="link7" file="link7.stl" />
<mesh name="base_link" file="base_link.stl" />
<mesh name="left_outer_knuckle" file="left_outer_knuckle.stl" />
<mesh name="left_finger" file="left_finger.stl" />
<mesh name="left_inner_knuckle" file="left_inner_knuckle.stl" />
<mesh name="right_outer_knuckle" file="right_outer_knuckle.stl" />
<mesh name="right_finger" file="right_finger.stl" />
<mesh name="right_inner_knuckle" file="right_inner_knuckle.stl" />
</asset>
<equality>
<weld body1="robot0:mocap2" body2="link7" solimp="0.9 0.95 0.001" solref="0.02 1"></weld>
</equality>
<default>
<joint armature="1" damping="0.1" limited="true"/>
<default class="robot0:blue">
<geom rgba="0.086 0.506 0.767 1.0"></geom>
</default>
<default class="robot0:grey">
<geom rgba="0.356 0.361 0.376 1.0"></geom>
</default>
</default>
</mujoco>

88
lerobot/common/envs/simxarm/simxarm/tasks/assets/xarm.xml
View File

@@ -1,88 +0,0 @@
<mujoco model="xarm7">
<body mocap="true" name="robot0:mocap2" pos="0 0 0">
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0.5 0 0" size="0.005 0.005 0.005" type="box"></geom>
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0.5 0 0 0" size="1 0.005 0.005" type="box"></geom>
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0 0.5 0" size="0.005 1 0.001" type="box"></geom>
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0.5 0.5 0 0" size="0.005 0.005 1" type="box"></geom>
</body>
<body name="link0" pos="1.09 0.28 0.655">
<geom name="bb" type="mesh" mesh="link_base" material="robot0:base_mat" rgba="1 1 1 1"/>
<body name="link1" pos="0 0 0.267">
<inertial pos="-0.0042142 0.02821 -0.0087788" quat="0.917781 -0.277115 0.0606681 0.277858" mass="0.42603" diaginertia="0.00144551 0.00137757 0.000823511" />
<joint name="joint1" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="10" frictionloss="1" />
<geom name="j1" type="mesh" mesh="link1" material="robot0:arm_mat" rgba="1 1 1 1"/>
<body name="link2" pos="0 0 0" quat="0.707105 -0.707108 0 0">
<inertial pos="-3.3178e-05 -0.12849 0.026337" quat="0.447793 0.894132 -0.00224061 0.00218314" mass="0.56095" diaginertia="0.00319151 0.00311598 0.000980804" />
<joint name="joint2" pos="0 0 0" axis="0 0 1" limited="true" range="-2.059 2.0944" damping="10" frictionloss="1" />
<geom name="j2" type="mesh" mesh="link2" material="robot0:head_mat" rgba="1 1 1 1"/>
<body name="link3" pos="0 -0.293 0" quat="0.707105 0.707108 0 0">
<inertial pos="0.04223 -0.023258 -0.0096674" quat="0.883205 0.339803 0.323238 0.000542237" mass="0.44463" diaginertia="0.00133227 0.00119126 0.000780475" />
<joint name="joint3" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="5" frictionloss="1" />
<geom name="j3" type="mesh" mesh="link3" material="robot0:gripper_mat" rgba="1 1 1 1"/>
<body name="link4" pos="0.0525 0 0" quat="0.707105 0.707108 0 0">
<inertial pos="0.067148 -0.10732 0.024479" quat="0.0654142 0.483317 -0.738663 0.465298" mass="0.52387" diaginertia="0.00288984 0.00282705 0.000894409" />
<joint name="joint4" pos="0 0 0" axis="0 0 1" limited="true" range="-0.19198 3.927" damping="5" frictionloss="1" />
<geom name="j4" type="mesh" mesh="link4" material="robot0:arm_mat" rgba="1 1 1 1"/>
<body name="link5" pos="0.0775 -0.3425 0" quat="0.707105 0.707108 0 0">
<inertial pos="-0.00023397 0.036705 -0.080064" quat="0.981064 -0.19003 0.00637998 0.0369004" mass="0.18554" diaginertia="0.00099553 0.000988613 0.000247126" />
<joint name="joint5" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="5" frictionloss="1" />
<geom name="j5" type="mesh" material="robot0:gripper_mat" rgba="1 1 1 1" mesh="link5" />
<body name="link6" pos="0 0 0" quat="0.707105 0.707108 0 0">
<inertial pos="0.058911 0.028469 0.0068428" quat="-0.188705 0.793535 0.166088 0.554173" mass="0.31344" diaginertia="0.000827892 0.000768871 0.000386708" />
<joint name="joint6" pos="0 0 0" axis="0 0 1" limited="true" range="-1.69297 3.14159" damping="2" frictionloss="1" />
<geom name="j6" type="mesh" material="robot0:gripper_mat" rgba="1 1 1 1" mesh="link6" />
<body name="link7" pos="0.076 0.097 0" quat="0.707105 -0.707108 0 0">
<inertial pos="-0.000420033 -0.00287433 0.0257078" quat="0.999372 -0.0349129 -0.00605634 0.000551744" mass="0.85624" diaginertia="0.00137671 0.00118744 0.000514968" />
<joint name="joint7" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="2" frictionloss="1" />
<geom name="j8" material="robot0:gripper_mat" type="mesh" rgba="0.753 0.753 0.753 1" mesh="link7" />
<geom name="j9" material="robot0:gripper_mat" type="mesh" rgba="1 1 1 1" mesh="base_link" />
<site name="grasp" pos="0 0 0.16" rgba="1 0 0 0" type="sphere" size="0.01" group="1"/>
<body name="left_outer_knuckle" pos="0 0.035 0.059098">
<inertial pos="0 0.021559 0.015181" quat="0.47789 0.87842 0 0" mass="0.033618" diaginertia="1.9111e-05 1.79089e-05 1.90167e-06" />
<joint name="drive_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="left_outer_knuckle" />
<body name="left_finger" pos="0 0.035465 0.042039">
<inertial pos="0 -0.016413 0.029258" quat="0.697634 0.115353 -0.115353 0.697634" mass="0.048304" diaginertia="1.88037e-05 1.7493e-05 3.56792e-06" />
<joint name="left_finger_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
<geom name="j10" material="robot0:gripper_finger_mat" type="mesh" rgba="0 0 0 1" conaffinity="3" contype="2" mesh="left_finger" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
<body name="right_hand" pos="0 -0.03 0.05" quat="-0.7071 0 0 0.7071">
<site name="ee" pos="0 0 0" rgba="0 0 1 0" type="sphere" group="1"/>
<site name="ee_x" pos="0 0 0" size="0.005 .1" quat="0.707105 0.707108 0 0 " rgba="1 0 0 0" type="cylinder" group="1"/>
<site name="ee_z" pos="0 0 0" size="0.005 .1" quat="0.707105 0 0 0.707108" rgba="0 0 1 0" type="cylinder" group="1"/>
<site name="ee_y" pos="0 0 0" size="0.005 .1" quat="0.707105 0 0.707108 0 " rgba="0 1 0 0" type="cylinder" group="1"/>
</body>
</body>
</body>
<body name="left_inner_knuckle" pos="0 0.02 0.074098">
<inertial pos="1.86601e-06 0.0220468 0.0261335" quat="0.664139 -0.242732 0.242713 0.664146" mass="0.0230126" diaginertia="8.34216e-06 6.0949e-06 2.75601e-06" />
<joint name="left_inner_knuckle_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="left_inner_knuckle" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
</body>
<body name="right_outer_knuckle" pos="0 -0.035 0.059098">
<inertial pos="0 -0.021559 0.015181" quat="0.87842 0.47789 0 0" mass="0.033618" diaginertia="1.9111e-05 1.79089e-05 1.90167e-06" />
<joint name="right_outer_knuckle_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="right_outer_knuckle" />
<body name="right_finger" pos="0 -0.035465 0.042039">
<inertial pos="0 0.016413 0.029258" quat="0.697634 -0.115356 0.115356 0.697634" mass="0.048304" diaginertia="1.88038e-05 1.7493e-05 3.56779e-06" />
<joint name="right_finger_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
<geom name="j11" material="robot0:gripper_finger_mat" type="mesh" rgba="0 0 0 1" conaffinity="3" contype="2" mesh="right_finger" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
<body name="left_hand" pos="0 0.03 0.05" quat="-0.7071 0 0 0.7071">
<site name="ee_2" pos="0 0 0" rgba="1 0 0 0" type="sphere" size="0.01" group="1"/>
</body>
</body>
</body>
<body name="right_inner_knuckle" pos="0 -0.02 0.074098">
<inertial pos="1.866e-06 -0.022047 0.026133" quat="0.66415 0.242702 -0.242721 0.664144" mass="0.023013" diaginertia="8.34209e-06 6.0949e-06 2.75601e-06" />
<joint name="right_inner_knuckle_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="right_inner_knuckle" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</mujoco>

148
lerobot/common/envs/simxarm/simxarm/tasks/base.py
View File

@@ -1,148 +0,0 @@
import os
import mujoco
import numpy as np
from gymnasium_robotics.envs import robot_env
from lerobot.common.envs.simxarm.simxarm.tasks import mocap
class Base(robot_env.MujocoRobotEnv):
"""
Superclass for all simxarm environments.
Args:
xml_name (str): name of the xml environment file
gripper_rotation (list): initial rotation of the gripper (given as a quaternion)
"""
def __init__(self, xml_name, gripper_rotation=None):
if gripper_rotation is None:
gripper_rotation = [0, 1, 0, 0]
self.gripper_rotation = np.array(gripper_rotation, dtype=np.float32)
self.center_of_table = np.array([1.655, 0.3, 0.63625])
self.max_z = 1.2
self.min_z = 0.2
super().__init__(
model_path=os.path.join(os.path.dirname(__file__), "assets", xml_name + ".xml"),
n_substeps=20,
n_actions=4,
initial_qpos={},
)
@property
def dt(self):
return self.n_substeps * self.model.opt.timestep
@property
def eef(self):
return self._utils.get_site_xpos(self.model, self.data, "grasp")
@property
def obj(self):
return self._utils.get_site_xpos(self.model, self.data, "object_site")
@property
def robot_state(self):
gripper_angle = self._utils.get_joint_qpos(self.model, self.data, "right_outer_knuckle_joint")
return np.concatenate([self.eef, gripper_angle])
def is_success(self):
return NotImplementedError()
def get_reward(self):
raise NotImplementedError()
def _sample_goal(self):
raise NotImplementedError()
def get_obs(self):
return self._get_obs()
def _step_callback(self):
self._mujoco.mj_forward(self.model, self.data)
def _limit_gripper(self, gripper_pos, pos_ctrl):
if gripper_pos[0] > self.center_of_table[0] - 0.105 + 0.15:
pos_ctrl[0] = min(pos_ctrl[0], 0)
if gripper_pos[0] < self.center_of_table[0] - 0.105 - 0.3:
pos_ctrl[0] = max(pos_ctrl[0], 0)
if gripper_pos[1] > self.center_of_table[1] + 0.3:
pos_ctrl[1] = min(pos_ctrl[1], 0)
if gripper_pos[1] < self.center_of_table[1] - 0.3:
pos_ctrl[1] = max(pos_ctrl[1], 0)
if gripper_pos[2] > self.max_z:
pos_ctrl[2] = min(pos_ctrl[2], 0)
if gripper_pos[2] < self.min_z:
pos_ctrl[2] = max(pos_ctrl[2], 0)
return pos_ctrl
def _apply_action(self, action):
assert action.shape == (4,)
action = action.copy()
pos_ctrl, gripper_ctrl = action[:3], action[3]
pos_ctrl = self._limit_gripper(
self._utils.get_site_xpos(self.model, self.data, "grasp"), pos_ctrl
) * (1 / self.n_substeps)
gripper_ctrl = np.array([gripper_ctrl, gripper_ctrl])
mocap.apply_action(
self.model,
self._model_names,
self.data,
np.concatenate([pos_ctrl, self.gripper_rotation, gripper_ctrl]),
)
def _render_callback(self):
self._mujoco.mj_forward(self.model, self.data)
def _reset_sim(self):
self.data.time = self.initial_time
self.data.qpos[:] = np.copy(self.initial_qpos)
self.data.qvel[:] = np.copy(self.initial_qvel)
self._sample_goal()
self._mujoco.mj_step(self.model, self.data, nstep=10)
return True
def _set_gripper(self, gripper_pos, gripper_rotation):
self._utils.set_mocap_pos(self.model, self.data, "robot0:mocap", gripper_pos)
self._utils.set_mocap_quat(self.model, self.data, "robot0:mocap", gripper_rotation)
self._utils.set_joint_qpos(self.model, self.data, "right_outer_knuckle_joint", 0)
self.data.qpos[10] = 0.0
self.data.qpos[12] = 0.0
def _env_setup(self, initial_qpos):
for name, value in initial_qpos.items():
self.data.set_joint_qpos(name, value)
mocap.reset(self.model, self.data)
mujoco.mj_forward(self.model, self.data)
self._sample_goal()
mujoco.mj_forward(self.model, self.data)
def reset(self):
self._reset_sim()
return self._get_obs()
def step(self, action):
assert action.shape == (4,)
assert self.action_space.contains(action), "{!r} ({}) invalid".format(action, type(action))
self._apply_action(action)
self._mujoco.mj_step(self.model, self.data, nstep=2)
self._step_callback()
obs = self._get_obs()
reward = self.get_reward()
done = False
info = {"is_success": self.is_success(), "success": self.is_success()}
return obs, reward, done, info
def render(self, mode="rgb_array", width=384, height=384):
self._render_callback()
# HACK
self.model.vis.global_.offwidth = width
self.model.vis.global_.offheight = height
if mode in self.mujoco_renderer._viewers:
self.mujoco_renderer._viewers.get(mode).viewport.width = width
self.mujoco_renderer._viewers.get(mode).viewport.height = height
return self.mujoco_renderer.render(mode)
def close(self):
if self.mujoco_renderer is not None:
self.mujoco_renderer.close()

100
lerobot/common/envs/simxarm/simxarm/tasks/lift.py
View File

@@ -1,100 +0,0 @@
import numpy as np
from lerobot.common.envs.simxarm.simxarm import Base
class Lift(Base):
def __init__(self):
self._z_threshold = 0.15
super().__init__("lift")
@property
def z_target(self):
return self._init_z + self._z_threshold
def is_success(self):
return self.obj[2] >= self.z_target
def get_reward(self):
reach_dist = np.linalg.norm(self.obj - self.eef)
reach_dist_xy = np.linalg.norm(self.obj[:-1] - self.eef[:-1])
pick_completed = self.obj[2] >= (self.z_target - 0.01)
obj_dropped = (self.obj[2] < (self._init_z + 0.005)) and (reach_dist > 0.02)
# Reach
if reach_dist < 0.05:
reach_reward = -reach_dist + max(self._action[-1], 0) / 50
elif reach_dist_xy < 0.05:
reach_reward = -reach_dist
else:
z_bonus = np.linalg.norm(np.linalg.norm(self.obj[-1] - self.eef[-1]))
reach_reward = -reach_dist - 2 * z_bonus
# Pick
if pick_completed and not obj_dropped:
pick_reward = self.z_target
elif (reach_dist < 0.1) and (self.obj[2] > (self._init_z + 0.005)):
pick_reward = min(self.z_target, self.obj[2])
else:
pick_reward = 0
return reach_reward / 100 + pick_reward
def _get_obs(self):
eef_velp = self._utils.get_site_xvelp(self.model, self.data, "grasp") * self.dt
gripper_angle = self._utils.get_joint_qpos(self.model, self.data, "right_outer_knuckle_joint")
eef = self.eef - self.center_of_table
obj = self.obj - self.center_of_table
obj_rot = self._utils.get_joint_qpos(self.model, self.data, "object_joint0")[-4:]
obj_velp = self._utils.get_site_xvelp(self.model, self.data, "object_site") * self.dt
obj_velr = self._utils.get_site_xvelr(self.model, self.data, "object_site") * self.dt
obs = np.concatenate(
[
eef,
eef_velp,
obj,
obj_rot,
obj_velp,
obj_velr,
eef - obj,
np.array(
[
np.linalg.norm(eef - obj),
np.linalg.norm(eef[:-1] - obj[:-1]),
self.z_target,
self.z_target - obj[-1],
self.z_target - eef[-1],
]
),
gripper_angle,
],
axis=0,
)
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": eef}
def _sample_goal(self):
# Gripper
gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
super()._set_gripper(gripper_pos, self.gripper_rotation)
# Object
object_pos = self.center_of_table - np.array([0.15, 0.10, 0.07])
object_pos[0] += self.np_random.uniform(-0.05, 0.05, size=1)
object_pos[1] += self.np_random.uniform(-0.05, 0.05, size=1)
object_qpos = self._utils.get_joint_qpos(self.model, self.data, "object_joint0")
object_qpos[:3] = object_pos
self._utils.set_joint_qpos(self.model, self.data, "object_joint0", object_qpos)
self._init_z = object_pos[2]
# Goal
return object_pos + np.array([0, 0, self._z_threshold])
def reset(self):
self._action = np.zeros(4)
return super().reset()
def step(self, action):
self._action = action.copy()
return super().step(action)

67
lerobot/common/envs/simxarm/simxarm/tasks/mocap.py
View File

@@ -1,67 +0,0 @@
# import mujoco_py
import mujoco
import numpy as np
def apply_action(model, model_names, data, action):
if model.nmocap > 0:
pos_action, gripper_action = np.split(action, (model.nmocap * 7,))
if data.ctrl is not None:
for i in range(gripper_action.shape[0]):
data.ctrl[i] = gripper_action[i]
pos_action = pos_action.reshape(model.nmocap, 7)
pos_delta, quat_delta = pos_action[:, :3], pos_action[:, 3:]
reset_mocap2body_xpos(model, model_names, data)
data.mocap_pos[:] = data.mocap_pos + pos_delta
data.mocap_quat[:] = data.mocap_quat + quat_delta
def reset(model, data):
if model.nmocap > 0 and model.eq_data is not None:
for i in range(model.eq_data.shape[0]):
# if sim.model.eq_type[i] == mujoco_py.const.EQ_WELD:
if model.eq_type[i] == mujoco.mjtEq.mjEQ_WELD:
# model.eq_data[i, :] = np.array([0., 0., 0., 1., 0., 0., 0.])
model.eq_data[i, :] = np.array(
[
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
)
# sim.forward()
mujoco.mj_forward(model, data)
def reset_mocap2body_xpos(model, model_names, data):
if model.eq_type is None or model.eq_obj1id is None or model.eq_obj2id is None:
return
# For all weld constraints
for eq_type, obj1_id, obj2_id in zip(model.eq_type, model.eq_obj1id, model.eq_obj2id, strict=False):
# if eq_type != mujoco_py.const.EQ_WELD:
if eq_type != mujoco.mjtEq.mjEQ_WELD:
continue
# body2 = model.body_id2name(obj2_id)
body2 = model_names.body_id2name[obj2_id]
if body2 == "B0" or body2 == "B9" or body2 == "B1":
continue
mocap_id = model.body_mocapid[obj1_id]
if mocap_id != -1:
# obj1 is the mocap, obj2 is the welded body
body_idx = obj2_id
else:
# obj2 is the mocap, obj1 is the welded body
mocap_id = model.body_mocapid[obj2_id]
body_idx = obj1_id
assert mocap_id != -1
data.mocap_pos[mocap_id][:] = data.xpos[body_idx]
data.mocap_quat[mocap_id][:] = data.xquat[body_idx]

86
lerobot/common/envs/simxarm/simxarm/tasks/peg_in_box.py
View File

@@ -1,86 +0,0 @@
import numpy as np
from lerobot.common.envs.simxarm.simxarm import Base
class PegInBox(Base):
def __init__(self):
super().__init__("peg_in_box")
def _reset_sim(self):
self._act_magnitude = 0
super()._reset_sim()
for _ in range(10):
self._apply_action(np.array([0, 0, 0, 1], dtype=np.float32))
self.sim.step()
@property
def box(self):
return self.sim.data.get_site_xpos("box_site")
def is_success(self):
return np.linalg.norm(self.obj - self.box) <= 0.05
def get_reward(self):
dist_xy = np.linalg.norm(self.obj[:2] - self.box[:2])
dist_xyz = np.linalg.norm(self.obj - self.box)
return float(dist_xy <= 0.045) * (2 - 6 * dist_xyz) - 0.2 * np.square(self._act_magnitude) - dist_xy
def _get_obs(self):
eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
eef, box = self.eef - self.center_of_table, self.box - self.center_of_table
obj = self.obj - self.center_of_table
obj_rot = self.sim.data.get_joint_qpos("object_joint0")[-4:]
obj_velp = self.sim.data.get_site_xvelp("object_site") * self.dt
obj_velr = self.sim.data.get_site_xvelr("object_site") * self.dt
obs = np.concatenate(
[
eef,
eef_velp,
box,
obj,
obj_rot,
obj_velp,
obj_velr,
eef - box,
eef - obj,
obj - box,
np.array(
[
np.linalg.norm(eef - box),
np.linalg.norm(eef - obj),
np.linalg.norm(obj - box),
gripper_angle,
]
),
],
axis=0,
)
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": box}
def _sample_goal(self):
# Gripper
gripper_pos = np.array([1.280, 0.295, 0.9]) + self.np_random.uniform(-0.05, 0.05, size=3)
super()._set_gripper(gripper_pos, self.gripper_rotation)
# Object
object_pos = gripper_pos - np.array([0, 0, 0.06]) + self.np_random.uniform(-0.005, 0.005, size=3)
object_qpos = self.sim.data.get_joint_qpos("object_joint0")
object_qpos[:3] = object_pos
self.sim.data.set_joint_qpos("object_joint0", object_qpos)
# Box
box_pos = np.array([1.61, 0.18, 0.58])
box_pos[:2] += self.np_random.uniform(-0.11, 0.11, size=2)
box_qpos = self.sim.data.get_joint_qpos("box_joint0")
box_qpos[:3] = box_pos
self.sim.data.set_joint_qpos("box_joint0", box_qpos)
return self.box
def step(self, action):
self._act_magnitude = np.linalg.norm(action[:3])
return super().step(action)

78
lerobot/common/envs/simxarm/simxarm/tasks/push.py
View File

@@ -1,78 +0,0 @@
import numpy as np
from lerobot.common.envs.simxarm.simxarm import Base
class Push(Base):
def __init__(self):
super().__init__("push")
def _reset_sim(self):
self._act_magnitude = 0
super()._reset_sim()
def is_success(self):
return np.linalg.norm(self.obj - self.goal) <= 0.05
def get_reward(self):
dist = np.linalg.norm(self.obj - self.goal)
penalty = self._act_magnitude**2
return -(dist + 0.15 * penalty)
def _get_obs(self):
eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
eef, goal = self.eef - self.center_of_table, self.goal - self.center_of_table
obj = self.obj - self.center_of_table
obj_rot = self.sim.data.get_joint_qpos("object_joint0")[-4:]
obj_velp = self.sim.data.get_site_xvelp("object_site") * self.dt
obj_velr = self.sim.data.get_site_xvelr("object_site") * self.dt
obs = np.concatenate(
[
eef,
eef_velp,
goal,
obj,
obj_rot,
obj_velp,
obj_velr,
eef - goal,
eef - obj,
obj - goal,
np.array(
[
np.linalg.norm(eef - goal),
np.linalg.norm(eef - obj),
np.linalg.norm(obj - goal),
gripper_angle,
]
),
],
axis=0,
)
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": goal}
def _sample_goal(self):
# Gripper
gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
super()._set_gripper(gripper_pos, self.gripper_rotation)
# Object
object_pos = self.center_of_table - np.array([0.25, 0, 0.07])
object_pos[0] += self.np_random.uniform(-0.08, 0.08, size=1)
object_pos[1] += self.np_random.uniform(-0.08, 0.08, size=1)
object_qpos = self.sim.data.get_joint_qpos("object_joint0")
object_qpos[:3] = object_pos
self.sim.data.set_joint_qpos("object_joint0", object_qpos)
# Goal
self.goal = np.array([1.600, 0.200, 0.545])
self.goal[:2] += self.np_random.uniform(-0.1, 0.1, size=2)
self.sim.model.site_pos[self.sim.model.site_name2id("target0")] = self.goal
return self.goal
def step(self, action):
self._act_magnitude = np.linalg.norm(action[:3])
return super().step(action)

44
lerobot/common/envs/simxarm/simxarm/tasks/reach.py
View File

@@ -1,44 +0,0 @@
import numpy as np
from lerobot.common.envs.simxarm.simxarm import Base
class Reach(Base):
def __init__(self):
super().__init__("reach")
def _reset_sim(self):
self._act_magnitude = 0
super()._reset_sim()
def is_success(self):
return np.linalg.norm(self.eef - self.goal) <= 0.05
def get_reward(self):
dist = np.linalg.norm(self.eef - self.goal)
penalty = self._act_magnitude**2
return -(dist + 0.15 * penalty)
def _get_obs(self):
eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
eef, goal = self.eef - self.center_of_table, self.goal - self.center_of_table
obs = np.concatenate(
[eef, eef_velp, goal, eef - goal, np.array([np.linalg.norm(eef - goal), gripper_angle])], axis=0
)
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": goal}
def _sample_goal(self):
# Gripper
gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
super()._set_gripper(gripper_pos, self.gripper_rotation)
# Goal
self.goal = np.array([1.550, 0.287, 0.580])
self.goal[:2] += self.np_random.uniform(-0.125, 0.125, size=2)
self.sim.model.site_pos[self.sim.model.site_name2id("target0")] = self.goal
return self.goal
def step(self, action):
self._act_magnitude = np.linalg.norm(action[:3])
return super().step(action)

0
lerobot/common/transforms.py → lerobot/common/envs/transforms.py
View File

6
lerobot/common/logger.py
View File

@@ -30,7 +30,6 @@ class Logger:
self._model_dir = self._log_dir / "models"
self._buffer_dir = self._log_dir / "buffers"
self._save_model = cfg.save_model
self._disable_wandb_artifact = cfg.wandb.disable_artifact
self._save_buffer = cfg.save_buffer
self._group = cfg_to_group(cfg)
self._seed = cfg.seed
@@ -72,10 +71,9 @@ class Logger:
self._model_dir.mkdir(parents=True, exist_ok=True)
fp = self._model_dir / f"{str(identifier)}.pt"
policy.save(fp)
if self._wandb and not self._disable_wandb_artifact:
# note wandb artifact does not accept ":" in its name
if self._wandb:
artifact = self._wandb.Artifact(
self._group.replace(":", "_") + "-" + str(self._seed) + "-" + str(identifier),
self._group + "-" + str(self._seed) + "-" + str(identifier),
type="model",
)
artifact.add_file(fp)

82
lerobot/common/policies/abstract.py
View File

@@ -1,82 +0,0 @@
from collections import deque
import torch
from torch import Tensor, nn
class AbstractPolicy(nn.Module):
"""Base policy which all policies should be derived from.
The forward method should generally not be overriden as it plays the role of handling multi-step policies. See its
documentation for more information.
Note:
When implementing a concrete class (e.g. `AlohaDataset`, `PushtEnv`, `DiffusionPolicy`), you need to:
1. set the required class attributes:
- for classes inheriting from `AbstractDataset`: `available_datasets`
- for classes inheriting from `AbstractEnv`: `name`, `available_tasks`
- for classes inheriting from `AbstractPolicy`: `name`
2. update variables in `lerobot/__init__.py` (e.g. `available_envs`, `available_datasets_per_envs`, `available_policies`)
3. update variables in `tests/test_available.py` by importing your new class
"""
name: str | None = None # same name should be used to instantiate the policy in factory.py
def __init__(self, n_action_steps: int | None):
"""
n_action_steps: Sets the cache size for storing action trajectories. If None, it is assumed that a single
action is returned by `select_actions` and that doesn't have a horizon dimension. The `forward` method then
adds that dimension.
"""
super().__init__()
assert self.name is not None, "Subclasses of `AbstractPolicy` should set the `name` class attribute."
self.n_action_steps = n_action_steps
self.clear_action_queue()
def update(self, replay_buffer, step):
"""One step of the policy's learning algorithm."""
raise NotImplementedError("Abstract method")
def save(self, fp):
torch.save(self.state_dict(), fp)
def load(self, fp):
d = torch.load(fp)
self.load_state_dict(d)
def select_actions(self, observation) -> Tensor:
"""Select an action (or trajectory of actions) based on an observation during rollout.
If n_action_steps was provided at initialization, this should return a (batch_size, n_action_steps, *) tensor of
actions. Otherwise if n_actions_steps is None, this should return a (batch_size, *) tensor of actions.
"""
raise NotImplementedError("Abstract method")
def clear_action_queue(self):
"""This should be called whenever the environment is reset."""
if self.n_action_steps is not None:
self._action_queue = deque([], maxlen=self.n_action_steps)
def forward(self, *args, **kwargs) -> Tensor:
"""Inference step that makes multi-step policies compatible with their single-step environments.
WARNING: In general, this should not be overriden.
Consider a "policy" that observes the environment then charts a course of N actions to take. To make this fit
into the formalism of a TorchRL environment, we view it as being effectively a policy that (1) makes an
observation and prepares a queue of actions, (2) consumes that queue when queried, regardless of the environment
observation, (3) repopulates the action queue when empty. This method handles the aforementioned logic so that
the subclass doesn't have to.
This method effectively wraps the `select_actions` method of the subclass. The following assumptions are made:
1. The `select_actions` method returns a Tensor of actions with shape (B, H, *) where B is the batch size, H is
the action trajectory horizon and * is the action dimensions.
2. Prior to the `select_actions` method being called, theres is an `n_action_steps` instance attribute defined.
"""
if self.n_action_steps is None:
return self.select_actions(*args, **kwargs)
if len(self._action_queue) == 0:
# `select_actions` returns a (batch_size, n_action_steps, *) tensor, but the queue effectively has shape
# (n_action_steps, batch_size, *), hence the transpose.
self._action_queue.extend(self.select_actions(*args, **kwargs).transpose(0, 1))
return self._action_queue.popleft()

25
lerobot/common/policies/act/policy.py
View File

@@ -2,12 +2,11 @@ import logging
import time
import torch
import torch.nn as nn
import torch.nn.functional as F # noqa: N812
import torchvision.transforms as transforms
from lerobot.common.policies.abstract import AbstractPolicy
from lerobot.common.policies.act.detr_vae import build
from lerobot.common.utils import get_safe_torch_device
def build_act_model_and_optimizer(cfg):
@@ -41,14 +40,12 @@ def kl_divergence(mu, logvar):
return total_kld, dimension_wise_kld, mean_kld
class ActionChunkingTransformerPolicy(AbstractPolicy):
name = "act"
class ActionChunkingTransformerPolicy(nn.Module):
def __init__(self, cfg, device, n_action_steps=1):
super().__init__(n_action_steps)
super().__init__()
self.cfg = cfg
self.n_action_steps = n_action_steps
self.device = get_safe_torch_device(device)
self.device = device
self.model, self.optimizer = build_act_model_and_optimizer(cfg)
self.kl_weight = self.cfg.kl_weight
logging.info(f"KL Weight {self.kl_weight}")
@@ -150,15 +147,16 @@ class ActionChunkingTransformerPolicy(AbstractPolicy):
return loss
@torch.no_grad()
def select_actions(self, observation, step_count):
if observation["image"].shape[0] != 1:
raise NotImplementedError("Batch size > 1 not handled")
def forward(self, observation, step_count):
# TODO(rcadene): remove unused step_count
del step_count
self.eval()
# TODO(rcadene): remove unsqueeze hack to add bsize=1
observation["image", "top"] = observation["image", "top"].unsqueeze(0)
# observation["state"] = observation["state"].unsqueeze(0)
# TODO(rcadene): remove hack
# add 1 camera dimension
observation["image", "top"] = observation["image", "top"].unsqueeze(1)
@@ -182,8 +180,11 @@ class ActionChunkingTransformerPolicy(AbstractPolicy):
# exp_weights = torch.from_numpy(exp_weights).cuda().unsqueeze(dim=1)
# raw_action = (actions_for_curr_step * exp_weights).sum(dim=0, keepdim=True)
# remove bsize=1
action = action.squeeze(0)
# take first predicted action or n first actions
action = action[: self.n_action_steps]
action = action[0] if self.n_action_steps == 1 else action[: self.n_action_steps]
return action
def _forward(self, qpos, image, actions=None, is_pad=None):

1
lerobot/common/policies/act/position_encoding.py
View File

@@ -1,7 +1,6 @@
"""
Various positional encodings for the transformer.
"""
import math
import torch

1
lerobot/common/policies/act/transformer.py
View File

@@ -6,7 +6,6 @@ Copy-paste from torch.nn.Transformer with modifications:
* extra LN at the end of encoder is removed
* decoder returns a stack of activations from all decoding layers
"""
import copy
from typing import Optional

1
lerobot/common/policies/act/utils.py
View File

@@ -3,7 +3,6 @@ Misc functions, including distributed helpers.
Mostly copy-paste from torchvision references.
"""
import datetime
import os
import pickle

44
lerobot/common/policies/diffusion/diffusion_unet_image_policy.py
View File

@@ -1,44 +1,3 @@
"""Code from the original diffusion policy project.
Notes on how to load a checkpoint from the original repository:
In the original repository, run the eval and use a breakpoint to extract the policy weights.
```
torch.save(policy.state_dict(), "weights.pt")
```
In this repository, add a breakpoint somewhere after creating an equivalent policy and load in the weights:
```
loaded = torch.load("weights.pt")
aligned = {}
their_prefix = "obs_encoder.obs_nets.image.backbone"
our_prefix = "obs_encoder.key_model_map.image.backbone"
aligned.update({our_prefix + k.removeprefix(their_prefix): v for k, v in loaded.items() if k.startswith(their_prefix)})
their_prefix = "obs_encoder.obs_nets.image.pool"
our_prefix = "obs_encoder.key_model_map.image.pool"
aligned.update({our_prefix + k.removeprefix(their_prefix): v for k, v in loaded.items() if k.startswith(their_prefix)})
their_prefix = "obs_encoder.obs_nets.image.nets.3"
our_prefix = "obs_encoder.key_model_map.image.out"
aligned.update({our_prefix + k.removeprefix(their_prefix): v for k, v in loaded.items() if k.startswith(their_prefix)})
aligned.update({k: v for k, v in loaded.items() if k.startswith('model.')})
# Note: here you are loading into the ema model.
missing_keys, unexpected_keys = policy.ema_diffusion.load_state_dict(aligned, strict=False)
assert all('_dummy_variable' in k for k in missing_keys)
assert len(unexpected_keys) == 0
```
Then in that same runtime you can also save the weights with the new aligned state_dict:
```
policy.save("weights.pt")
```
Now you can remove the breakpoint and extra code and load in the weights just like with any other lerobot checkpoint.
"""
from typing import Dict
import torch
@@ -231,10 +190,11 @@ class DiffusionUnetImagePolicy(BaseImagePolicy):
# run sampling
nsample = self.conditional_sample(
cond_data, cond_mask, local_cond=local_cond, global_cond=global_cond
cond_data, cond_mask, local_cond=local_cond, global_cond=global_cond, **self.kwargs
)
action_pred = nsample[..., :action_dim]
# get action
start = n_obs_steps - 1
end = start + self.n_action_steps

55
lerobot/common/policies/diffusion/model/multi_image_obs_encoder.py
View File

@@ -1,40 +1,15 @@
import copy
from typing import Dict, Optional, Tuple, Union
from typing import Dict, Tuple, Union
import torch
import torch.nn as nn
import torchvision
from robomimic.models.base_nets import ResNet18Conv, SpatialSoftmax
from lerobot.common.policies.diffusion.model.crop_randomizer import CropRandomizer
from lerobot.common.policies.diffusion.model.module_attr_mixin import ModuleAttrMixin
from lerobot.common.policies.diffusion.pytorch_utils import replace_submodules
class RgbEncoder(nn.Module):
"""Following `VisualCore` from Robomimic 0.2.0."""
def __init__(self, input_shape, relu=True, pretrained=False, num_keypoints=32):
"""
input_shape: channel-first input shape (C, H, W)
resnet_name: a timm model name.
pretrained: whether to use timm pretrained weights.
relu: whether to use relu as a final step.
num_keypoints: Number of keypoints for SpatialSoftmax (default value of 32 matches PushT Image).
"""
super().__init__()
self.backbone = ResNet18Conv(input_channel=input_shape[0], pretrained=pretrained)
# Figure out the feature map shape.
with torch.inference_mode():
feat_map_shape = tuple(self.backbone(torch.zeros(size=(1, *input_shape))).shape[1:])
self.pool = SpatialSoftmax(feat_map_shape, num_kp=num_keypoints)
self.out = nn.Linear(num_keypoints * 2, num_keypoints * 2)
self.relu = nn.ReLU() if relu else nn.Identity()
def forward(self, x):
return self.relu(self.out(torch.flatten(self.pool(self.backbone(x)), start_dim=1)))
class MultiImageObsEncoder(ModuleAttrMixin):
def __init__(
self,
@@ -49,7 +24,7 @@ class MultiImageObsEncoder(ModuleAttrMixin):
share_rgb_model: bool = False,
# renormalize rgb input with imagenet normalization
# assuming input in [0,1]
norm_mean_std: Optional[tuple[float, float]] = None,
imagenet_norm: bool = False,
):
"""
Assumes rgb input: B,C,H,W
@@ -123,9 +98,10 @@ class MultiImageObsEncoder(ModuleAttrMixin):
this_normalizer = torchvision.transforms.CenterCrop(size=(h, w))
# configure normalizer
this_normalizer = nn.Identity()
if norm_mean_std is not None:
if imagenet_norm:
# TODO(rcadene): move normalizer to dataset and env
this_normalizer = torchvision.transforms.Normalize(
mean=norm_mean_std[0], std=norm_mean_std[1]
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
this_transform = nn.Sequential(this_resizer, this_randomizer, this_normalizer)
@@ -148,17 +124,6 @@ class MultiImageObsEncoder(ModuleAttrMixin):
def forward(self, obs_dict):
batch_size = None
features = []
# process lowdim input
for key in self.low_dim_keys:
data = obs_dict[key]
if batch_size is None:
batch_size = data.shape[0]
else:
assert batch_size == data.shape[0]
assert data.shape[1:] == self.key_shape_map[key]
features.append(data)
# process rgb input
if self.share_rgb_model:
# pass all rgb obs to rgb model
@@ -196,6 +161,16 @@ class MultiImageObsEncoder(ModuleAttrMixin):
feature = self.key_model_map[key](img)
features.append(feature)
# process lowdim input
for key in self.low_dim_keys:
data = obs_dict[key]
if batch_size is None:
batch_size = data.shape[0]
else:
assert batch_size == data.shape[0]
assert data.shape[1:] == self.key_shape_map[key]
features.append(data)
# concatenate all features
result = torch.cat(features, dim=-1)
return result

1
lerobot/common/policies/diffusion/model/tensor_utils.py
View File

@@ -2,7 +2,6 @@
A collection of utilities for working with nested tensor structures consisting
of numpy arrays and torch tensors.
"""
import collections
import numpy as np

51
lerobot/common/policies/diffusion/policy.py
View File

@@ -1,20 +1,16 @@
import copy
import logging
import time
import hydra
import torch
import torch.nn as nn
from lerobot.common.policies.abstract import AbstractPolicy
from lerobot.common.policies.diffusion.diffusion_unet_image_policy import DiffusionUnetImagePolicy
from lerobot.common.policies.diffusion.model.lr_scheduler import get_scheduler
from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder, RgbEncoder
from lerobot.common.utils import get_safe_torch_device
from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder
class DiffusionPolicy(AbstractPolicy):
name = "diffusion"
class DiffusionPolicy(nn.Module):
def __init__(
self,
cfg,
@@ -38,14 +34,11 @@ class DiffusionPolicy(AbstractPolicy):
# parameters passed to step
**kwargs,
):
super().__init__(n_action_steps)
super().__init__()
self.cfg = cfg
noise_scheduler = hydra.utils.instantiate(cfg_noise_scheduler)
rgb_model_input_shape = copy.deepcopy(shape_meta.obs.image.shape)
if cfg_obs_encoder.crop_shape is not None:
rgb_model_input_shape[1:] = cfg_obs_encoder.crop_shape
rgb_model = RgbEncoder(input_shape=rgb_model_input_shape, **cfg_rgb_model)
rgb_model = hydra.utils.instantiate(cfg_rgb_model)
obs_encoder = MultiImageObsEncoder(
rgb_model=rgb_model,
**cfg_obs_encoder,
@@ -69,16 +62,15 @@ class DiffusionPolicy(AbstractPolicy):
**kwargs,
)
self.device = get_safe_torch_device(cfg_device)
self.diffusion.to(self.device)
self.device = torch.device(cfg_device)
if torch.cuda.is_available() and cfg_device == "cuda":
self.diffusion.cuda()
self.ema_diffusion = None
self.ema = None
if self.cfg.use_ema:
self.ema_diffusion = copy.deepcopy(self.diffusion)
self.ema = hydra.utils.instantiate(
cfg_ema,
model=self.ema_diffusion,
model=copy.deepcopy(self.diffusion),
)
self.optimizer = hydra.utils.instantiate(
@@ -101,22 +93,21 @@ class DiffusionPolicy(AbstractPolicy):
)
@torch.no_grad()
def select_actions(self, observation, step_count):
"""
Note: this uses the ema model weights if self.training == False, otherwise the non-ema model weights.
"""
def forward(self, observation, step_count):
# TODO(rcadene): remove unused step_count
del step_count
# TODO(rcadene): remove unsqueeze hack to add bsize=1
observation["image"] = observation["image"].unsqueeze(0)
observation["state"] = observation["state"].unsqueeze(0)
obs_dict = {
"image": observation["image"],
"agent_pos": observation["state"],
}
if self.training:
out = self.diffusion.predict_action(obs_dict)
else:
out = self.ema_diffusion.predict_action(obs_dict)
action = out["action"]
out = self.diffusion.predict_action(obs_dict)
action = out["action"].squeeze(0)
return action
def update(self, replay_buffer, step):
@@ -205,10 +196,4 @@ class DiffusionPolicy(AbstractPolicy):
def load(self, fp):
d = torch.load(fp)
missing_keys, unexpected_keys = self.load_state_dict(d, strict=False)
if len(missing_keys) > 0:
assert all(k.startswith("ema_diffusion.") for k in missing_keys)
logging.warning(
"DiffusionPolicy.load expected ema parameters in loaded state dict but none were found."
)
assert len(unexpected_keys) == 0
self.load_state_dict(d)

18
lerobot/common/policies/factory.py
View File

@@ -1,11 +1,8 @@
def make_policy(cfg):
if cfg.policy.name != "diffusion" and cfg.rollout_batch_size > 1:
raise NotImplementedError("Only diffusion policy supports rollout_batch_size > 1 for the time being.")
if cfg.policy.name == "tdmpc":
from lerobot.common.policies.tdmpc.policy import TDMPCPolicy
from lerobot.common.policies.tdmpc.policy import TDMPC
policy = TDMPCPolicy(cfg.policy, cfg.device)
policy = TDMPC(cfg.policy, cfg.device)
elif cfg.policy.name == "diffusion":
from lerobot.common.policies.diffusion.policy import DiffusionPolicy
@@ -26,22 +23,15 @@ def make_policy(cfg):
policy = ActionChunkingTransformerPolicy(
cfg.policy, cfg.device, n_action_steps=cfg.n_action_steps + cfg.n_latency_steps
)
# if cfg.env.name == "aloha" and cfg.env.task == "sim_transfer_cube_scripted":
# import torch
# state_dict = torch.load(
# "/home/rcadene/code/act/tmp/2024_03_10_sim_transfer_cube_scripted/policy_best.ckpt"
# )
# policy.load_state_dict(state_dict)
else:
raise ValueError(cfg.policy.name)
if cfg.policy.pretrained_model_path:
# TODO(rcadene): hack for old pretrained models from fowm
if cfg.policy.name == "tdmpc" and "fowm" in cfg.policy.pretrained_model_path:
if "offline" in cfg.policy.pretrained_model_path:
if "offline" in cfg.pretrained_model_path:
policy.step[0] = 25000
elif "final" in cfg.policy.pretrained_model_path:
elif "final" in cfg.pretrained_model_path:
policy.step[0] = 100000
else:
raise NotImplementedError()

2
lerobot/common/policies/tdmpc/helper.py
View File

@@ -152,7 +152,7 @@ def enc(cfg):
if cfg.modality in {"pixels", "all"}:
C = int(3 * cfg.frame_stack) # noqa: N806
pixels_enc_layers = [
nn.Identity(), # NormalizeImg(), # TODO(rcadene): we need to clean this
NormalizeImg(),
nn.Conv2d(C, cfg.num_channels, 7, stride=2),
nn.ReLU(),
nn.Conv2d(cfg.num_channels, cfg.num_channels, 5, stride=2),

26
lerobot/common/policies/tdmpc/policy.py
View File

@@ -9,8 +9,6 @@ import torch
import torch.nn as nn
import lerobot.common.policies.tdmpc.helper as h
from lerobot.common.policies.abstract import AbstractPolicy
from lerobot.common.utils import get_safe_torch_device
FIRST_FRAME = 0
@@ -87,20 +85,17 @@ class TOLD(nn.Module):
return torch.min(Q1, Q2) if return_type == "min" else (Q1 + Q2) / 2
class TDMPCPolicy(AbstractPolicy):
class TDMPC(nn.Module):
"""Implementation of TD-MPC learning + inference."""
name = "tdmpc"
def __init__(self, cfg, device):
super().__init__(None)
super().__init__()
self.action_dim = cfg.action_dim
self.cfg = cfg
self.device = get_safe_torch_device(device)
self.device = torch.device(device)
self.std = h.linear_schedule(cfg.std_schedule, 0)
self.model = TOLD(cfg)
self.model.to(self.device)
self.model = TOLD(cfg).cuda() if torch.cuda.is_available() and device == "cuda" else TOLD(cfg)
self.model_target = deepcopy(self.model)
self.optim = torch.optim.Adam(self.model.parameters(), lr=self.cfg.lr)
self.pi_optim = torch.optim.Adam(self.model._pi.parameters(), lr=self.cfg.lr)
@@ -129,19 +124,20 @@ class TDMPCPolicy(AbstractPolicy):
self.model_target.load_state_dict(d["model_target"])
@torch.no_grad()
def select_actions(self, observation, step_count):
if observation["image"].shape[0] != 1:
raise NotImplementedError("Batch size > 1 not handled")
def forward(self, observation, step_count):
t0 = step_count.item() == 0
# TODO(rcadene): remove unsqueeze hack...
if observation["image"].ndim == 3:
observation["image"] = observation["image"].unsqueeze(0)
observation["state"] = observation["state"].unsqueeze(0)
obs = {
# TODO(rcadene): remove contiguous hack...
"rgb": observation["image"].contiguous(),
"state": observation["state"].contiguous(),
}
# Note: unsqueeze needed because `act` still uses non-batch logic.
action = self.act(obs, t0=t0, step=self.step.item()).unsqueeze(0)
action = self.act(obs, t0=t0, step=self.step.item())
return action
@torch.no_grad()

22
lerobot/common/utils.py
View File

@@ -6,27 +6,7 @@ import numpy as np
import torch
def get_safe_torch_device(cfg_device: str, log: bool = False) -> torch.device:
match cfg_device:
case "cuda":
assert torch.cuda.is_available()
device = torch.device("cuda")
case "mps":
assert torch.backends.mps.is_available()
device = torch.device("mps")
case "cpu":
device = torch.device("cpu")
if log:
logging.warning("Using CPU, this will be slow.")
case _:
device = torch.device(cfg_device)
if log:
logging.warning(f"Using custom {cfg_device} device.")
return device
def set_global_seed(seed):
def set_seed(seed):
"""Set seed for reproducibility."""
random.seed(seed)
np.random.seed(seed)

10
lerobot/configs/default.yaml
View File

@@ -5,14 +5,11 @@ defaults:
hydra:
run:
dir: outputs/train/${now:%Y-%m-%d}/${now:%H-%M-%S}_${env.name}_${policy.name}_${hydra.job.name}
dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}_${env.name}_${policy.name}_${hydra.job.name}
job:
name: default
seed: 1337
# batch size for TorchRL SerialEnv. Each underlying env will get the seed = seed + env_index
# NOTE: only diffusion policy supports rollout_batch_size > 1
rollout_batch_size: 1
device: cuda # cpu
prefetch: 4
eval_freq: ???
@@ -26,17 +23,12 @@ fps: ???
offline_prioritized_sampler: true
dataset_id: ???
n_action_steps: ???
n_obs_steps: ???
env: ???
policy: ???
wandb:
enable: true
# Set to true to disable saving an artifact despite save_model == True
disable_artifact: false
project: lerobot
notes: ""

4
lerobot/configs/env/aloha.yaml vendored
View File

@@ -10,11 +10,9 @@ online_steps: 25000
fps: 50
dataset_id: aloha_sim_insertion_human
env:
name: aloha
task: sim_insertion
task: sim_insertion_human
from_pixels: True
pixels_only: False
image_size: [3, 480, 640]

2
lerobot/configs/env/pusht.yaml vendored
View File

@@ -10,8 +10,6 @@ online_steps: 25000
fps: 10
dataset_id: pusht
env:
name: pusht
task: pusht

2
lerobot/configs/env/simxarm.yaml vendored
View File

@@ -9,8 +9,6 @@ online_steps: 25000
fps: 15
dataset_id: xarm_lift_medium
env:
name: simxarm
task: lift

51
lerobot/configs/policy/diffusion.yaml
View File

@@ -12,7 +12,6 @@ shape_meta:
action:
shape: [2]
seed: 100000
horizon: 16
n_obs_steps: 2
n_action_steps: 8
@@ -22,12 +21,12 @@ past_action_visible: False
keypoint_visible_rate: 1.0
obs_as_global_cond: True
eval_episodes: 50
eval_freq: 5000
save_freq: 5000
eval_episodes: 1
eval_freq: 10000
save_freq: 100000
log_freq: 250
offline_steps: 200000
offline_steps: 1344000
online_steps: 0
offline_prioritized_sampler: true
@@ -43,8 +42,8 @@ policy:
num_inference_steps: 100
obs_as_global_cond: ${obs_as_global_cond}
# crop_shape: null
diffusion_step_embed_dim: 128
down_dims: [512, 1024, 2048]
diffusion_step_embed_dim: 256 # before 128
down_dims: [256, 512, 1024] # before [512, 1024, 2048]
kernel_size: 5
n_groups: 8
cond_predict_scale: True
@@ -77,17 +76,17 @@ noise_scheduler:
obs_encoder:
shape_meta: ${shape_meta}
# resize_shape: null
crop_shape: [84, 84]
# crop_shape: [76, 76]
# constant center crop
random_crop: True
# random_crop: True
use_group_norm: True
share_rgb_model: False
norm_mean_std: [0.5, 0.5] # for PushT the original impl normalizes to [-1, 1] (maybe not the case for robomimic envs)
imagenet_norm: True
rgb_model:
pretrained: false
num_keypoints: 32
relu: true
_target_: lerobot.common.policies.diffusion.pytorch_utils.get_resnet
name: resnet18
weights: null
ema:
_target_: lerobot.common.policies.diffusion.model.ema_model.EMAModel
@@ -103,3 +102,29 @@ optimizer:
betas: [0.95, 0.999]
eps: 1.0e-8
weight_decay: 1.0e-6
training:
device: "cuda:0"
seed: 42
debug: False
resume: True
# optimization
# lr_scheduler: cosine
# lr_warmup_steps: 500
num_epochs: 8000
# gradient_accumulate_every: 1
# EMA destroys performance when used with BatchNorm
# replace BatchNorm with GroupNorm.
# use_ema: True
freeze_encoder: False
# training loop control
# in epochs
rollout_every: 50
checkpoint_every: 50
val_every: 1
sample_every: 5
# steps per epoch
max_train_steps: null
max_val_steps: null
# misc
tqdm_interval_sec: 1.0

1
lerobot/configs/policy/tdmpc.yaml
View File

@@ -1,7 +1,6 @@
# @package _global_
n_action_steps: 1
n_obs_steps: 1
policy:
name: tdmpc

234
lerobot/scripts/eval.py
View File

@@ -1,58 +1,21 @@
"""Evaluate a policy on an environment by running rollouts and computing metrics.
The script may be run in one of two ways:
1. By providing the path to a config file with the --config argument.
2. By providing a HuggingFace Hub ID with the --hub-id argument. You may also provide a revision number with the
--revision argument.
In either case, it is possible to override config arguments by adding a list of config.key=value arguments.
Examples:
You have a specific config file to go with trained model weights, and want to run 10 episodes.
```
python lerobot/scripts/eval.py \
--config PATH/TO/FOLDER/config.yaml \
policy.pretrained_model_path=PATH/TO/FOLDER/weights.pth \
eval_episodes=10
```
You have a HuggingFace Hub ID, you know which revision you want, and want to run 10 episodes (note that in this case,
you don't need to specify which weights to use):
```
python lerobot/scripts/eval.py --hub-id HUB/ID --revision v1.0 eval_episodes=10
```
"""
import argparse
import json
import logging
import os.path as osp
import threading
import time
from datetime import datetime as dt
from pathlib import Path
import einops
import hydra
import imageio
import numpy as np
import torch
import tqdm
from huggingface_hub import snapshot_download
from tensordict.nn import TensorDictModule
from torchrl.envs import EnvBase
from torchrl.envs.batched_envs import BatchedEnvBase
from lerobot.common.datasets.factory import make_offline_buffer
from lerobot.common.envs.factory import make_env
from lerobot.common.logger import log_output_dir
from lerobot.common.policies.abstract import AbstractPolicy
from lerobot.common.policies.factory import make_policy
from lerobot.common.utils import get_safe_torch_device, init_logging, set_global_seed
from lerobot.common.utils import init_logging, set_seed
def write_video(video_path, stacked_frames, fps):
@@ -60,8 +23,8 @@ def write_video(video_path, stacked_frames, fps):
def eval_policy(
env: BatchedEnvBase,
policy: AbstractPolicy,
env: EnvBase,
policy: TensorDictModule = None,
num_episodes: int = 10,
max_steps: int = 30,
save_video: bool = False,
@@ -69,137 +32,93 @@ def eval_policy(
fps: int = 15,
return_first_video: bool = False,
):
if policy is not None:
policy.eval()
start = time.time()
sum_rewards = []
max_rewards = []
successes = []
seeds = []
threads = [] # for video saving threads
episode_counter = 0 # for saving the correct number of videos
# TODO(alexander-soare): if num_episodes is not evenly divisible by the batch size, this will do more work than
# needed as I'm currently taking a ceil.
for i in tqdm.tqdm(range(-(-num_episodes // env.batch_size[0]))):
threads = []
for i in tqdm.tqdm(range(num_episodes)):
ep_frames = []
if save_video or (return_first_video and i == 0):
def maybe_render_frame(env: EnvBase, _):
if save_video or (return_first_video and i == 0): # noqa: B023
# TODO(rcadene): set width and height from config or env
# ep_frames.append(env.render(width=384, height=384)) # noqa: B023
def render_frame(env):
ep_frames.append(env.render()) # noqa: B023
# Clear the policy's action queue before the start of a new rollout.
if policy is not None:
policy.clear_action_queue()
env.register_rendering_hook(render_frame)
if env.is_closed:
env.start() # needed to be able to get the seeds the first time as BatchedEnvs are lazy
seeds.extend(env._next_seed)
with torch.inference_mode():
# TODO(alexander-soare): When `break_when_any_done == False` this rolls out for max_steps even when all
# envs are done the first time. But we only use the first rollout. This is a waste of compute.
rollout = env.rollout(
max_steps=max_steps,
policy=policy,
auto_cast_to_device=True,
callback=maybe_render_frame,
break_when_any_done=env.batch_size[0] == 1,
)
# Figure out where in each rollout sequence the first done condition was encountered (results after
# this won't be included).
# Note: this assumes that the shape of the done key is (batch_size, max_steps, 1).
# Note: this relies on a property of argmax: that it returns the first occurrence as a tiebreaker.
rollout_steps = rollout["next", "done"].shape[1]
done_indices = torch.argmax(rollout["next", "done"].to(int), axis=1) # (batch_size, rollout_steps)
mask = (torch.arange(rollout_steps) <= done_indices).unsqueeze(-1) # (batch_size, rollout_steps, 1)
batch_sum_reward = einops.reduce((rollout["next", "reward"] * mask), "b n 1 -> b", "sum")
batch_max_reward = einops.reduce((rollout["next", "reward"] * mask), "b n 1 -> b", "max")
batch_success = einops.reduce((rollout["next", "success"] * mask), "b n 1 -> b", "any")
sum_rewards.extend(batch_sum_reward.tolist())
max_rewards.extend(batch_max_reward.tolist())
successes.extend(batch_success.tolist())
# print(", ".join([f"{x:.3f}" for x in rollout["next", "reward"][:,0].tolist()]))
ep_sum_reward = rollout["next", "reward"].sum()
ep_max_reward = rollout["next", "reward"].max()
ep_success = rollout["next", "success"].any()
sum_rewards.append(ep_sum_reward.item())
max_rewards.append(ep_max_reward.item())
successes.append(ep_success.item())
if save_video or (return_first_video and i == 0):
batch_stacked_frames = np.stack(ep_frames) # (t, b, *)
batch_stacked_frames = batch_stacked_frames.transpose(
1, 0, *range(2, batch_stacked_frames.ndim)
) # (b, t, *)
stacked_frames = np.stack(ep_frames)
if save_video:
for stacked_frames, done_index in zip(
batch_stacked_frames, done_indices.flatten().tolist(), strict=False
):
if episode_counter >= num_episodes:
continue
video_dir.mkdir(parents=True, exist_ok=True)
video_path = video_dir / f"eval_episode_{episode_counter}.mp4"
thread = threading.Thread(
target=write_video,
args=(str(video_path), stacked_frames[:done_index], fps),
)
thread.start()
threads.append(thread)
episode_counter += 1
video_dir.mkdir(parents=True, exist_ok=True)
video_path = video_dir / f"eval_episode_{i}.mp4"
thread = threading.Thread(
target=write_video,
args=(str(video_path), stacked_frames, fps),
)
thread.start()
threads.append(thread)
if return_first_video and i == 0:
first_video = batch_stacked_frames[0].transpose(0, 3, 1, 2)
first_video = stacked_frames.transpose(0, 3, 1, 2)
env.reset_rendering_hooks()
for thread in threads:
thread.join()
info = {
"per_episode": [
{
"episode_ix": i,
"sum_reward": sum_reward,
"max_reward": max_reward,
"success": success,
"seed": seed,
}
for i, (sum_reward, max_reward, success, seed) in enumerate(
zip(
sum_rewards[:num_episodes],
max_rewards[:num_episodes],
successes[:num_episodes],
seeds[:num_episodes],
strict=True,
)
)
],
"aggregated": {
"avg_sum_reward": np.nanmean(sum_rewards[:num_episodes]),
"avg_max_reward": np.nanmean(max_rewards[:num_episodes]),
"pc_success": np.nanmean(successes[:num_episodes]) * 100,
"eval_s": time.time() - start,
"eval_ep_s": (time.time() - start) / num_episodes,
},
"avg_sum_reward": np.nanmean(sum_rewards),
"avg_max_reward": np.nanmean(max_rewards),
"pc_success": np.nanmean(successes) * 100,
"eval_s": time.time() - start,
"eval_ep_s": (time.time() - start) / num_episodes,
}
if return_first_video:
return info, first_video
return info
def eval(cfg: dict, out_dir=None, stats_path=None):
@hydra.main(version_base=None, config_name="default", config_path="../configs")
def eval_cli(cfg: dict):
eval(cfg, out_dir=hydra.core.hydra_config.HydraConfig.get().runtime.output_dir)
def eval(cfg: dict, out_dir=None):
if out_dir is None:
raise NotImplementedError()
init_logging()
# Check device is available
get_safe_torch_device(cfg.device, log=True)
if cfg.device == "cuda":
assert torch.cuda.is_available()
else:
logging.warning("Using CPU, this will be slow.")
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
set_global_seed(cfg.seed)
set_seed(cfg.seed)
log_output_dir(out_dir)
logging.info("Making transforms.")
offline_buffer = make_offline_buffer(cfg, stats_path=stats_path)
logging.info("make_offline_buffer")
offline_buffer = make_offline_buffer(cfg)
logging.info("Making environment.")
logging.info("make_env")
env = make_env(cfg, transform=offline_buffer.transform)
if cfg.policy.pretrained_model_path:
@@ -213,72 +132,19 @@ def eval(cfg: dict, out_dir=None, stats_path=None):
# when policy is None, rollout a random policy
policy = None
info = eval_policy(
metrics = eval_policy(
env,
policy=policy,
save_video=True,
video_dir=Path(out_dir) / "eval",
fps=cfg.env.fps,
max_steps=cfg.env.episode_length,
max_steps=cfg.env.episode_length // cfg.n_action_steps,
num_episodes=cfg.eval_episodes,
)
print(info["aggregated"])
# Save info
with open(Path(out_dir) / "eval_info.json", "w") as f:
json.dump(info, f, indent=2)
print(metrics)
logging.info("End of eval")
def _relative_path_between(path1: Path, path2: Path) -> Path:
"""Returns path1 relative to path2."""
path1 = path1.absolute()
path2 = path2.absolute()
try:
return path1.relative_to(path2)
except ValueError: # most likely because path1 is not a subpath of path2
common_parts = Path(osp.commonpath([path1, path2])).parts
return Path(
"/".join([".."] * (len(path2.parts) - len(common_parts)) + list(path1.parts[len(common_parts) :]))
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("--config", help="Path to a specific yaml config you want to use.")
group.add_argument("--hub-id", help="HuggingFace Hub ID for a pretrained model.")
parser.add_argument("--revision", help="Optionally provide the HuggingFace Hub revision ID.")
parser.add_argument(
"overrides",
nargs="*",
help="Any key=value arguments to override config values (use dots for.nested=overrides)",
)
args = parser.parse_args()
if args.config is not None:
# Note: For the config_path, Hydra wants a path relative to this script file.
hydra.initialize(
config_path=str(
_relative_path_between(Path(args.config).absolute().parent, Path(__file__).parent)
)
)
cfg = hydra.compose(Path(args.config).stem, args.overrides)
# TODO(alexander-soare): Save and load stats in trained model directory.
# stats_path = None
stats_path = "/home/rcadene/code/lerobot/outputs/pretrained_models/act_aloha_sim_transfer_cube_scripted/stats.pth"
elif args.hub_id is not None:
folder = Path(snapshot_download(args.hub_id, revision=args.revision))
cfg = hydra.initialize(config_path=str(_relative_path_between(folder, Path(__file__).parent)))
cfg = hydra.compose("config", args.overrides)
cfg.policy.pretrained_model_path = folder / "model.pt"
stats_path = folder / "stats.pth"
eval(
cfg,
out_dir=f"outputs/eval/{dt.now().strftime('%Y-%m-%d/%H-%M-%S')}_{cfg.env.name}_{cfg.policy.name}",
stats_path=stats_path,
)
eval_cli()

106
lerobot/scripts/train.py
View File

@@ -12,7 +12,7 @@ from lerobot.common.datasets.factory import make_offline_buffer
from lerobot.common.envs.factory import make_env
from lerobot.common.logger import Logger, log_output_dir
from lerobot.common.policies.factory import make_policy
from lerobot.common.utils import format_big_number, get_safe_torch_device, init_logging, set_global_seed
from lerobot.common.utils import format_big_number, init_logging, set_seed
from lerobot.scripts.eval import eval_policy
@@ -112,17 +112,17 @@ def train(cfg: dict, out_dir=None, job_name=None):
raise NotImplementedError()
if job_name is None:
raise NotImplementedError()
if cfg.online_steps > 0:
assert cfg.rollout_batch_size == 1, "rollout_batch_size > 1 not supported for online training steps"
init_logging()
# Check device is available
get_safe_torch_device(cfg.device, log=True)
if cfg.device == "cuda":
assert torch.cuda.is_available()
else:
logging.warning("Using CPU, this will be slow.")
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
set_global_seed(cfg.seed)
set_seed(cfg.seed)
logging.info("make_offline_buffer")
offline_buffer = make_offline_buffer(cfg)
@@ -155,7 +155,11 @@ def train(cfg: dict, out_dir=None, job_name=None):
num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
num_total_params = sum(p.numel() for p in policy.parameters())
td_policy = TensorDictModule(policy, in_keys=["observation", "step_count"], out_keys=["action"])
td_policy = TensorDictModule(
policy,
in_keys=["observation", "step_count"],
out_keys=["action"],
)
# log metrics to terminal and wandb
logger = Logger(out_dir, job_name, cfg)
@@ -170,30 +174,6 @@ def train(cfg: dict, out_dir=None, job_name=None):
logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
# Note: this helper will be used in offline and online training loops.
def _maybe_eval_and_maybe_save(step):
# if step % cfg.eval_freq == 0:
if True:
logging.info(f"Eval policy at step {step}")
eval_info, first_video = eval_policy(
env,
td_policy,
num_episodes=cfg.eval_episodes,
max_steps=cfg.env.episode_length,
return_first_video=True,
video_dir=Path(out_dir) / "eval",
save_video=True,
)
log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_buffer, is_offline)
if cfg.wandb.enable:
logger.log_video(first_video, step, mode="eval")
logging.info("Resume training")
if True:
# if cfg.save_model and step % cfg.save_freq == 0:
logging.info(f"Checkpoint policy after step {step}")
logger.save_model(policy, identifier=step)
logging.info("Resume training")
step = 0 # number of policy update (forward + backward + optim)
is_offline = True
@@ -201,14 +181,30 @@ def train(cfg: dict, out_dir=None, job_name=None):
if offline_step == 0:
logging.info("Start offline training on a fixed dataset")
# TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
policy.train()
train_info = policy.update(offline_buffer, step)
if step % cfg.log_freq == 0:
log_train_info(logger, train_info, step, cfg, offline_buffer, is_offline)
# Note: _maybe_eval_and_maybe_save happens **after** the `step`th training update has completed, so we pass in
# step + 1.
_maybe_eval_and_maybe_save(step + 1)
if step > 0 and step % cfg.eval_freq == 0:
logging.info(f"Eval policy at step {step}")
eval_info, first_video = eval_policy(
env,
td_policy,
num_episodes=cfg.eval_episodes,
max_steps=cfg.env.episode_length // cfg.n_action_steps,
return_first_video=True,
video_dir=Path(out_dir) / "eval",
save_video=True,
)
log_eval_info(logger, eval_info, step, cfg, offline_buffer, is_offline)
if cfg.wandb.enable:
logger.log_video(first_video, step, mode="eval")
logging.info("Resume training")
if step > 0 and cfg.save_model and step % cfg.save_freq == 0:
logging.info(f"Checkpoint policy at step {step}")
logger.save_model(policy, identifier=step)
logging.info("Resume training")
step += 1
@@ -221,26 +217,11 @@ def train(cfg: dict, out_dir=None, job_name=None):
# TODO: add configurable number of rollout? (default=1)
with torch.no_grad():
rollout = env.rollout(
max_steps=cfg.env.episode_length,
max_steps=cfg.env.episode_length // cfg.n_action_steps,
policy=td_policy,
auto_cast_to_device=True,
)
assert (
len(rollout.batch_size) == 2
), "2 dimensions expected: number of env in parallel x max number of steps during rollout"
num_parallel_env = rollout.batch_size[0]
if num_parallel_env != 1:
# TODO(rcadene): when num_parallel_env > 1, rollout["episode"] needs to be properly set and we need to add tests
raise NotImplementedError()
num_max_steps = rollout.batch_size[1]
assert num_max_steps <= cfg.env.episode_length
# reshape to have a list of steps to insert into online_buffer
rollout = rollout.reshape(num_parallel_env * num_max_steps)
assert len(rollout) <= cfg.env.episode_length // cfg.n_action_steps
# set same episode index for all time steps contained in this rollout
rollout["episode"] = torch.tensor([env_step] * len(rollout), dtype=torch.int)
online_buffer.extend(rollout)
@@ -266,9 +247,24 @@ def train(cfg: dict, out_dir=None, job_name=None):
train_info.update(rollout_info)
log_train_info(logger, train_info, step, cfg, offline_buffer, is_offline)
# Note: _maybe_eval_and_maybe_save happens **after** the `step`th training update has completed, so we pass
# in step + 1.
_maybe_eval_and_maybe_save(step + 1)
if step > 0 and step % cfg.eval_freq == 0:
logging.info(f"Eval policy at step {step}")
eval_info, first_video = eval_policy(
env,
td_policy,
num_episodes=cfg.eval_episodes,
max_steps=cfg.env.episode_length // cfg.n_action_steps,
return_first_video=True,
)
log_eval_info(logger, eval_info, step, cfg, offline_buffer, is_offline)
if cfg.wandb.enable:
logger.log_video(first_video, step, mode="eval")
logging.info("Resume training")
if step > 0 and cfg.save_model and step % cfg.save_freq == 0:
logging.info(f"Checkpoint policy at step {step}")
logger.save_model(policy, identifier=step)
logging.info("Resume training")
step += 1
online_step += 1

63
lerobot/scripts/visualize_dataset.py
View File

@@ -25,7 +25,6 @@ def visualize_dataset_cli(cfg: dict):
def cat_and_write_video(video_path, frames, fps):
# Expects images in [0, 255].
frames = torch.cat(frames)
assert frames.dtype == torch.uint8
frames = einops.rearrange(frames, "b c h w -> b h w c").numpy()
@@ -46,63 +45,44 @@ def visualize_dataset(cfg: dict, out_dir=None):
logging.info("make_offline_buffer")
offline_buffer = make_offline_buffer(
cfg,
overwrite_sampler=sampler,
# remove all transformations such as rescale images from [0,255] to [0,1] or normalization
normalize=False,
overwrite_batch_size=1,
overwrite_prefetch=12,
cfg, overwrite_sampler=sampler, normalize=False, overwrite_batch_size=1, overwrite_prefetch=12
)
logging.info("Start rendering episodes from offline buffer")
video_paths = render_dataset(offline_buffer, out_dir, MAX_NUM_STEPS * NUM_EPISODES_TO_RENDER, cfg.fps)
for video_path in video_paths:
logging.info(video_path)
def render_dataset(offline_buffer, out_dir, max_num_samples, fps):
out_dir = Path(out_dir)
video_paths = []
threads = []
frames = {}
current_ep_idx = 0
logging.info(f"Visualizing episode {current_ep_idx}")
for i in range(max_num_samples):
for _ in range(MAX_NUM_STEPS * NUM_EPISODES_TO_RENDER):
# TODO(rcadene): make it work with bsize > 1
ep_td = offline_buffer.sample(1)
ep_idx = ep_td["episode"][FIRST_FRAME].item()
# TODO(rcadene): modify offline_buffer._sampler._sample_list or sampler to randomly sample an episode, but sequentially sample frames
num_frames_left = offline_buffer._sampler._sample_list.numel()
episode_is_done = ep_idx != current_ep_idx
# TODO(rcaene): modify offline_buffer._sampler._sample_list or sampler to randomly sample an episode, but sequentially sample frames
no_more_frames = offline_buffer._sampler._sample_list.numel() == 0
new_episode = ep_idx != current_ep_idx
if episode_is_done:
logging.info(f"Rendering episode {current_ep_idx}")
if new_episode:
logging.info(f"Visualizing episode {current_ep_idx}")
for im_key in offline_buffer.image_keys:
if not episode_is_done and num_frames_left > 0 and i < (max_num_samples - 1):
# when first frame of episode, initialize frames dict
if im_key not in frames:
frames[im_key] = []
# add current frame to list of frames to render
frames[im_key].append(ep_td[im_key])
else:
# When episode has no more frame in its list of observation,
# one frame still remains. It is the result of the last action taken.
# It is stored in `"next"`, so we add it to the list of frames to render.
frames[im_key].append(ep_td["next"][im_key])
if new_episode or no_more_frames:
# append last observed frames (the ones after last action taken)
frames[im_key].append(ep_td[("next", *im_key)])
video_dir = Path(out_dir) / "visualize_dataset"
video_dir.mkdir(parents=True, exist_ok=True)
out_dir.mkdir(parents=True, exist_ok=True)
if len(offline_buffer.image_keys) > 1:
camera = im_key[-1]
video_path = out_dir / f"episode_{current_ep_idx}_{camera}.mp4"
video_path = video_dir / f"episode_{current_ep_idx}_{camera}.mp4"
else:
video_path = out_dir / f"episode_{current_ep_idx}.mp4"
video_paths.append(str(video_path))
video_path = video_dir / f"episode_{current_ep_idx}.mp4"
thread = threading.Thread(
target=cat_and_write_video,
args=(str(video_path), frames[im_key], fps),
args=(str(video_path), frames[im_key], cfg.fps),
)
thread.start()
threads.append(thread)
@@ -112,18 +92,19 @@ def render_dataset(offline_buffer, out_dir, max_num_samples, fps):
# reset list of frames
del frames[im_key]
if num_frames_left == 0:
logging.info("Ran out of frames")
break
# append current cameras images to list of frames
if im_key not in frames:
frames[im_key] = []
frames[im_key].append(ep_td[im_key])
if current_ep_idx == NUM_EPISODES_TO_RENDER:
if no_more_frames:
logging.info("Ran out of frames")
break
for thread in threads:
thread.join()
logging.info("End of visualize_dataset")
return video_paths
if __name__ == "__main__":

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650
poetry.lock generated
View File

@@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
[[package]]
name = "absl-py"
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[[package]]
name = "av"
version = "12.0.0"
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optional = false
python-versions = ">=3.8"
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@@ -2403,24 +2251,6 @@ tomli = {version = ">=1", markers = "python_version < \"3.11\""}
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@@ -2878,13 +2684,13 @@ test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov",
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testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
[[package]]
@@ -3143,58 +2947,9 @@ files = [
[package.dependencies]
mpmath = ">=0.19"
[[package]]
name = "tensorboard"
version = "2.16.2"
description = "TensorBoard lets you watch Tensors Flow"
optional = false
python-versions = ">=3.9"
files = [
{file = "tensorboard-2.16.2-py3-none-any.whl", hash = "sha256:9f2b4e7dad86667615c0e5cd072f1ea8403fc032a299f0072d6f74855775cc45"},
]
[package.dependencies]
absl-py = ">=0.4"
grpcio = ">=1.48.2"
markdown = ">=2.6.8"
numpy = ">=1.12.0"
protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
setuptools = ">=41.0.0"
six = ">1.9"
tensorboard-data-server = ">=0.7.0,<0.8.0"
werkzeug = ">=1.0.1"
[[package]]
name = "tensorboard-data-server"
version = "0.7.2"
description = "Fast data loading for TensorBoard"
optional = false
python-versions = ">=3.7"
files = [
{file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
{file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
{file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"},
]
[[package]]
name = "tensorboardx"
version = "2.6.2.2"
description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
optional = false
python-versions = "*"
files = [
{file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
{file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
]
[package.dependencies]
numpy = "*"
packaging = "*"
protobuf = ">=3.20"
[[package]]
name = "tensordict"
version = "0.4.0+b4c91e8"
version = "0.4.0+551331d"
description = ""
optional = false
python-versions = "*"
@@ -3215,7 +2970,7 @@ tests = ["pytest", "pytest-benchmark", "pytest-instafail", "pytest-rerunfailures
type = "git"
url = "https://github.com/pytorch/tensordict"
reference = "HEAD"
resolved_reference = "b4c91e8828c538ca0a50d8383fd99311a9afb078"
resolved_reference = "ed22554d6860731610df784b2f5d09f31d3dbc7a"
[[package]]
name = "termcolor"
@@ -3533,23 +3288,6 @@ perf = ["orjson"]
reports = ["pydantic (>=2.0.0)"]
sweeps = ["sweeps (>=0.2.0)"]
[[package]]
name = "werkzeug"
version = "3.0.1"
description = "The comprehensive WSGI web application library."
optional = false
python-versions = ">=3.8"
files = [
{file = "werkzeug-3.0.1-py3-none-any.whl", hash = "sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10"},
{file = "werkzeug-3.0.1.tar.gz", hash = "sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc"},
]
[package.dependencies]
MarkupSafe = ">=2.1.1"
[package.extras]
watchdog = ["watchdog (>=2.3)"]
[[package]]
name = "zarr"
version = "2.17.1"
@@ -3573,20 +3311,20 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
[[package]]
name = "zipp"
version = "3.18.1"
version = "3.17.0"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
{file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
{file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
{file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
]
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "174c7d42f8039eedd2c447a4e6cae5169782cbd94346b5606572a0010194ca05"
content-hash = "ee86b84a795e6a3e9c2d79f244a87b55589adbe46d549ac38adf48be27c04cf9"

12
pyproject.toml
View File

@@ -21,6 +21,7 @@ packages = [{include = "lerobot"}]
[tool.poetry.dependencies]
python = "^3.10"
cython = "^3.0.8"
termcolor = "^2.4.0"
omegaconf = "^2.3.0"
dm-env = "^1.6"
@@ -41,24 +42,21 @@ mpmath = "^1.3.0"
torch = "^2.2.1"
tensordict = {git = "https://github.com/pytorch/tensordict"}
torchrl = {git = "https://github.com/pytorch/rl", rev = "13bef426dcfa5887c6e5034a6e9697993fa92c37"}
mujoco = "^2.3.7"
mujoco = "2.3.7"
mujoco-py = "^2.1.2.14"
gym = "^0.26.2"
opencv-python = "^4.9.0.80"
diffusers = "^0.26.3"
torchvision = "^0.17.1"
h5py = "^3.10.0"
dm-control = "1.0.14"
huggingface-hub = {extras = ["hf-transfer"], version = "^0.21.4"}
robomimic = "0.2.0"
gymnasium-robotics = "^1.2.4"
gymnasium = "^0.29.1"
cmake = "^3.29.0.1"
[tool.poetry.group.dev.dependencies]
pre-commit = "^3.6.2"
debugpy = "^1.8.1"
pytest = "^8.1.0"
pytest-cov = "^5.0.0"
[tool.ruff]
@@ -91,7 +89,7 @@ exclude = [
[tool.ruff.lint]
select = ["E4", "E7", "E9", "F", "I", "N", "B", "C4", "SIM"]
ignore-init-module-imports = true
[tool.poetry-dynamic-versioning]
enable = true

0
tests/data/aloha_sim_insertion_human/replay_buffer/action.memmap → tests/data/aloha_sim_insertion_human/action.memmap
View File

0
tests/data/aloha_sim_insertion_human/replay_buffer/episode.memmap → tests/data/aloha_sim_insertion_human/episode.memmap
View File

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