metadata

datasets:
  - pepijn223/lekiwi1750840522
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
  - robotics
  - act
model_summary: >-
  [Action Chunking with Transformers
  (ACT)](https://huggingface.co/papers/2304.13705) 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.

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 LeRobot Docs.

How to Get Started with the Model

For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:

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