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--- |
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license: apache-2.0 |
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datasets: |
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- lerobot/pusht_keypoints |
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pipeline_tag: robotics |
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--- |
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# Model Card for Diffusion Policy / PushT (keypoints) |
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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) with keypoint-only observations. |
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Note: The original work trains keypoints-only with conditioning via inpainting. Here, we encode the observation along with the agent position and use the encoding as global conditioning for the denoising U-Net. |
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## How to Get Started with the Model |
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Use `python lerobot/scripts/eval.py -p lerobot/diffusion_pusht` to evaluate for 50 episodes with the outputs sent to `outputs/eval`. |
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For further information, please see the [LeRobot library](https://github.com/huggingface/lerobot) (particularly the [evaluation script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py)). |
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## Training Details |
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Trained with [LeRobot@cc2f6e7](https://github.com/huggingface/lerobot/tree/cc2f6e74047bd65db0f9705fa602636b625bc28c). |
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The model was trained using [LeRobot's training script]([TODO link with commit hash one the PR is merged](https://github.com/huggingface/lerobot/blob/cc2f6e74047bd65db0f9705fa602636b625bc28c/lerobot/scripts/train.py)) and with the [pusht_keypoints](https://huggingface.co/datasets/lerobot/pusht_keypoints/tree/v1.5) dataset, using this command: |
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```bash |
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python lerobot/scripts/train.py \ |
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hydra.job.name=diffusion_pusht_keypoints \ |
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hydra.run.dir=outputs/train/2024-07-03/13-52-44_diffusion_pusht_keypoints \ |
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env=pusht_keypoints \ |
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policy=diffusion_pusht_keypoints \ |
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training.save_checkpoint=true \ |
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training.offline_steps=200000 \ |
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training.save_freq=20000 \ |
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training.eval_freq=10000 \ |
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training.log_freq=50 \ |
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training.num_workers=4 \ |
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eval.n_episodes=50 \ |
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eval.batch_size=50 \ |
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wandb.enable=true \ |
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wandb.disable_artifact=true \ |
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device=cuda \ |
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use_amp=true |
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``` |
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The training curves may be found at https://wandb.ai/alexander-soare/lerobot/runs/5z9d8q9q/overview. |
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This took about 5 hours to train on an Nvida RTX H100. |
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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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Metric|Average over 500 episodes |
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Average max. overlap ratio | 0.97 |
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Success rate (%) | 71.0 |
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The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json). |
