| --- |
| datasets: arclabmit/xarm7_beavrsim_shellgame_dataset |
| library_name: lerobot |
| license: apache-2.0 |
| pipeline_tag: robotics |
| tags: |
| - robotics |
| - lerobot |
| - act |
| model-index: |
| - name: xarm7_act_beavrsim_shellgame_model |
| results: |
| - task: |
| type: robotics |
| name: Robotic Manipulation |
| dataset: |
| name: beavr_sim |
| type: simulation |
| metrics: |
| - type: success_rate |
| value: 10.299999999999999 |
| name: Success Rate |
| - type: reward |
| value: -0.05 |
| name: Avg Reward |
| --- |
| |
| # Model Card for act |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
|
|
|
|
| [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. |
|
|
|
|
| This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). |
| See the full documentation at [LeRobot Docs](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 on how to train and run inference/eval: |
|
|
| ### Train from scratch |
|
|
| ```bash |
| lerobot-train \ |
| --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/`._ |
|
|
| ### Evaluate the policy/run inference |
|
|
| ```bash |
| 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 |
| |
| |
| <!-- EVAL_RESULTS_START --> |
| ## Evaluation Results |
| *Evaluated on 2026-02-05 09:45* |
| |
| | Metric | Value | |
| | :--- | :--- | |
| | **Success Rate** | 10.3% | |
| | **Average Reward** | -0.050 | |
| | **Max Reward (Avg)** | 1.030 | |
| | **Episodes** | 1000 | |
| | **Eval Speed** | 2.47 s/ep | |
| | **Seed** | 26 | |
| |
| > [!TIP] |
| > Detailed per-episode results can be found in [eval/eval_info.json](./eval/eval_info.json). |
| |
| <!-- EVAL_RESULTS_END --> |
| |
