LeRobot

Installation

Install dependencies using conda:

conda env create -f environment.yaml
conda activate lerobot

dev

python setup.py develop

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

• priority update doesnt match FOWM or original paper
• self.step=100000 should be updated at every step to adjust to horizon of planner
• prefetch replay buffer to speedup training
• parallelize env to speedup eval
• clean checkpointing / loading
• clean logging
• clean config
• clean hyperparameter tuning
• add pusht
• add aloha
• add act
• add diffusion
• add aloha 2

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

isort lerobot && isort tests && black lerobot && black tests
pylint lerobot && pylint tests  # not enforce for now

Tests

pytest -sx tests