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--- |
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datasets: lerobot/pusht |
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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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- lerobot |
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- robotics |
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- act |
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--- |
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# Model Card for act |
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ACT trained on PushT dataset for 80,000 training steps. |
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Trained for 3.5 hrs on T4 GPU on Colab free. |
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The goal is to use this to compare ACTs performance on PushT against diffusion policy particularly the aspect of action multimodality. |
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Training logs: https://api.wandb.ai/links/ramachandranaadarsh-indian-institute-of-technology-madras/7m9dpcgw |
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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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lerobot-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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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 |
