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Add Aloha env and ACT policy

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WIP Aloha env tests pass

Rendering works (fps look fast tho? TODO action bounding is too wide [-1,1])

Update README

Copy past from act repo

Remove download.py add a WIP for Simxarm

Remove download.py add a WIP for Simxarm

Add act yaml (TODO: try train.py)

Training can runs (TODO: eval)

Add tasks without end_effector that are compatible with dataset, Eval can run (TODO: training and pretrained model)

Add AbstractEnv, Refactor AlohaEnv, Add rendering_hook in env, Minor modifications, (TODO: Refactor Pusht and Simxarm)

poetry lock

fix bug in compute_stats for action normalization

fix more bugs in normalization

fix training

fix import

PushtEnv inheriates AbstractEnv, Improve factory Normalization

Add _make_env to EnvAbstract

Add call_rendering_hooks to pusht env

SimxarmEnv inherites from AbstractEnv (NOT TESTED)

Add aloha tests artifacts + update pusht stats

fix image normalization: before env was in [0,1] but dataset in [0,255], and now both in [0,255]

Small fix on simxarm

Add next to obs

Add top camera to Aloha env (TODO: make it compatible with set of cameras)

Add top camera to Aloha env (TODO: make it compatible with set of cameras)
This commit is contained in:
Remi Cadene
2024-03-08 09:47:39 +00:00
committed by Cadene
parent 060bac7672
commit 9d002032d1
116 changed files with 3658 additions and 301 deletions
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28
lerobot/common/datasets/factory.py
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@@ -5,7 +5,7 @@ from pathlib import Path
import torch
from torchrl.data.replay_buffers import PrioritizedSliceSampler, SliceSampler
from lerobot.common.envs.transforms import NormalizeTransform
from lerobot.common.envs.transforms import NormalizeTransform, Prod
DATA_DIR = Path(os.environ.get("DATA_DIR", "data"))
@@ -84,6 +84,16 @@ def make_offline_buffer(
prefetch=prefetch if isinstance(prefetch, int) else None,
)
if cfg.policy.name == "tdmpc":
img_keys = []
for key in offline_buffer.image_keys:
img_keys.append(("next", *key))
img_keys += offline_buffer.image_keys
else:
img_keys = offline_buffer.image_keys
transforms = [Prod(in_keys=img_keys, prod=1 / 255)]
if normalize:
# 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()
@@ -92,11 +102,10 @@ def make_offline_buffer(
in_keys = [("observation", "state"), ("action")]
if cfg.policy.name == "tdmpc":
for key in offline_buffer.image_keys:
# TODO(rcadene): imagenet normalization is applied inside diffusion policy, but no normalization inside tdmpc
in_keys.append(key)
# since we use next observations in tdmpc
in_keys.append(("next", *key))
# TODO(rcadene): we add img_keys to the keys to normalize for tdmpc only, since diffusion and act policies normalize the image inside the model for now
in_keys += img_keys
# TODO(racdene): since we use next observations in tdmpc, we also add them to the normalization. We are wasting a bit of compute on this for now.
in_keys += [("next", *key) for key in img_keys]
in_keys.append(("next", "observation", "state"))
if cfg.policy.name == "diffusion" and cfg.env.name == "pusht":
@@ -106,8 +115,11 @@ 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)
transform = NormalizeTransform(stats, in_keys, mode="min_max")
offline_buffer.set_transform(transform)
# 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)
if not overwrite_sampler:
index = torch.arange(0, offline_buffer.num_samples, 1)