9d002032d1
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) |
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| .github/workflows | ||
| lerobot | ||
| tests | ||
| .gitattributes | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| LICENSE | ||
| README.md | ||
| poetry.lock | ||
| pyproject.toml | ||
| sbatch.sh | ||
| sbatch_hopper.sh | ||
README.md
LeRobot
Installation
Create a virtual environment with Python 3.10, e.g. using conda:
conda create -y -n lerobot python=3.10
conda activate lerobot
Install poetry (if you don't have it already)
curl -sSL https://install.python-poetry.org | python -
Install dependencies
poetry install
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'
To use Weights and Biases for experiments tracking, log in with
wandb login
Usage
Train
python lerobot/scripts/train.py \
hydra.job.name=pusht \
env=pusht
Visualize offline buffer
python lerobot/scripts/visualize_dataset.py \
hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
env=pusht
Visualize online buffer / Eval
python lerobot/scripts/eval.py \
hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
env=pusht
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
Ask Remi Cadene for access if needed.
Profile
Example
from torch.profiler import profile, record_function, ProfilerActivity
def trace_handler(prof):
prof.export_chrome_trace(f"tmp/trace_schedule_{prof.step_num}.json")
with profile(
activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
schedule=torch.profiler.schedule(
wait=2,
warmup=2,
active=3,
),
on_trace_ready=trace_handler
) as prof:
with record_function("eval_policy"):
for i in range(num_episodes):
prof.step()
python lerobot/scripts/eval.py \
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
Tests
Install git lfs 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
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
Acknowledgment
- Our Diffusion policy and Pusht environment are adapted from Diffusion Policy
- Our TDMPC policy and Simxarm environment are adapted from FOWM
- Our ACT policy and ALOHA environment are adapted from ALOHA