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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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17
tests/test_envs.py
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@@ -1,6 +1,8 @@
import pytest
from tensordict import TensorDict
import torch
from torchrl.envs.utils import check_env_specs, step_mdp
from lerobot.common.datasets.factory import make_offline_buffer
from lerobot.common.envs.factory import make_env
from lerobot.common.envs.pusht.env import PushtEnv
@@ -83,9 +85,24 @@ def test_pusht(from_pixels, pixels_only):
[
# "simxarm",
"pusht",
"aloha",
],
)
def test_factory(env_name):
cfg = init_config(overrides=[f"env={env_name}"])
offline_buffer = make_offline_buffer(cfg)
env = make_env(cfg)
for key in offline_buffer.image_keys:
assert env.reset().get(key).dtype == torch.uint8
check_env_specs(env)
env = make_env(cfg, transform=offline_buffer.transform)
for key in offline_buffer.image_keys:
img = env.reset().get(key)
assert img.dtype == torch.float32
# TODO(rcadene): we assume for now that image normalization takes place in the model
assert img.max() <= 1.0
assert img.min() >= 0.0
check_env_specs(env)