Model Card for act
Action Chunking with Transformers (ACT) is a imitation-learning method that, predicts short action chunks instead of single steps. It learns from tele-operated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at https://huggingface.co/docs/lerobot/index.
How to Get Started with the Model
For a complete walkthrough, see the training guide → https://huggingface.co/docs/lerobot/il_robots#train-a-policy. Below is the short version for the two tasks you’ll run most:
1 Train from scratch
python lerobot/scripts/train.py \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
Writes checkpoints to outputs/train/<desired_policy_repo_id>/checkpoints/.
2 Evaluate the policy
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.
Model Details
- License: apache-2.0
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