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---
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datasets: Heuzef/rectangle_v1
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library_name: lerobot
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license: apache-2.0
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model_name: act
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pipeline_tag: robotics
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tags:
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- robotics
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- act
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- lerobot
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---
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# Model Card for act
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<!-- Provide a quick summary of what the model is/does. -->
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[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
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---
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## How to Get Started with the Model
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For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
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Below is the short version on how to train and run inference/eval:
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### Train from scratch
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```bash
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python -m lerobot.scripts.train \
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--dataset.repo_id=${HF_USER}/<dataset> \
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--policy.type=act \
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--output_dir=outputs/train/<desired_policy_repo_id> \
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--job_name=lerobot_training \
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--policy.device=cuda \
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--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
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--wandb.enable=true
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```
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_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
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### Evaluate the policy/run inference
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```bash
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python -m lerobot.record \
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--robot.type=so100_follower \
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--dataset.repo_id=<hf_user>/eval_<dataset> \
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--policy.path=<hf_user>/<desired_policy_repo_id> \
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--episodes=10
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```
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Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
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---
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## Model Details
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- **License:** apache-2.0 |
