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--- |
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datasets: arclabmit/xarm7_beavrsim_shellgame_dataset |
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library_name: lerobot |
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license: apache-2.0 |
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pipeline_tag: robotics |
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tags: |
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- robotics |
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- lerobot |
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- act |
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model-index: |
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- name: xarm7_act_beavrsim_shellgame_model |
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results: |
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- task: |
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type: robotics |
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name: Robotic Manipulation |
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dataset: |
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name: beavr_sim |
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type: simulation |
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metrics: |
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- type: success_rate |
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value: 10.299999999999999 |
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name: Success Rate |
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- type: reward |
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value: -0.05 |
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name: Avg Reward |
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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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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 |
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<!-- EVAL_RESULTS_START --> |
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## Evaluation Results |
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*Evaluated on 2026-02-05 09:45* |
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| Metric | Value | |
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| :--- | :--- | |
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| **Success Rate** | 10.3% | |
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| **Average Reward** | -0.050 | |
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| **Max Reward (Avg)** | 1.030 | |
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| **Episodes** | 1000 | |
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| **Eval Speed** | 2.47 s/ep | |
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| **Seed** | 26 | |
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> [!TIP] |
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> Detailed per-episode results can be found in [eval/eval_info.json](./eval/eval_info.json). |
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<!-- EVAL_RESULTS_END --> |
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