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--- |
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license: apache-2.0 |
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datasets: |
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- JayLee131/vqbet_pusht |
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pipeline_tag: robotics |
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--- |
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# Model Card for VQ-BeT/PushT |
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VQ-BeT (as per [Behavior Generation with Latent Actions](https://arxiv.org/abs/2403.03181)) trained for the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht). |
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## How to Get Started with the Model |
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See the [LeRobot library](https://github.com/huggingface/lerobot) (particularly the [evaluation script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py)) for instructions on how to load and evaluate this model. |
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## Training Details |
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The model was trained using this command: |
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```bash |
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python lerobot/scripts/train.py \ |
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policy=vqbet \ |
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env=pusht dataset_repo_id=lerobot/pusht \ |
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wandb.enable=true \ |
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device=cuda |
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``` |
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This took about 7 hours to train on an Nvida A6000. |
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## Model Size |
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<blank>|Number of Parameters |
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RGB Encoder | 11.2M |
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Remaining VQ-BeT Parts | 26.3M |
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## Evaluation |
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The model was evaluated on the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht). There are two evaluation metrics on a per-episode basis: |
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- Maximum overlap with target (seen as `eval/avg_max_reward` in the charts above). This ranges in [0, 1]. |
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- Success: whether or not the maximum overlap is at least 95%. |
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Here are the metrics for 500 episodes worth of evaluation. |
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<blank>|Ours |
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-|- |
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Average max. overlap ratio for 500 episodes | 0.887 |
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Success rate for 500 episodes (%) | 66.0 |
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The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json). |
