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--- |
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license: apache-2.0 |
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datasets: |
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- lerobot/pusht |
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pipeline_tag: robotics |
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--- |
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# Model Card for Transformer based Diffusion Policy / PushT |
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Transformer based Diffusion Policy (as per [Diffusion Policy: Visuomotor Policy |
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Learning via Action Diffusion](https://arxiv.org/abs/2303.04137)) 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 [LeRobot's training script](https://github.com/huggingface/lerobot/blob/d747195c5733c4f68d4bfbe62632d6fc1b605712/lerobot/scripts/train.py) and with the [pusht](https://huggingface.co/datasets/lerobot/pusht/tree/v1.3) dataset, using this command: |
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```bash |
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python lerobot/scripts/train.py \ |
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hydra.run.dir=outputs/train/diffusion_pusht \ |
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hydra.job.name=diffusion_pusht \ |
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policy=diffusion training.save_model=true \ |
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env=pusht \ |
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env.task=PushT-v0 \ |
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dataset_repo_id=lerobot/pusht \ |
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training.save_freq=25000 \ |
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training.eval_freq=10000 \ |
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wandb.enable=true \ |
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device=cuda |
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``` |
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The training and eval curves may be found at https://api.wandb.ai/links/none7/rd2trav7 |
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This took about 6 hours to train on an Nvida Tesla P100 GPU. |
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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) and compared to a similar model trained with the original [Diffusion Policy code](https://github.com/real-stanford/diffusion_policy). 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|Theirs |
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-|-|- |
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Average max. overlap ratio | 0.000 | 0.000 |
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Success rate for 500 episodes (%) | 0.00 | 0.00 |
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The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json). |
