README.md

---
license: apache-2.0
datasets:
- lerobot/pusht_keypoints
pipeline_tag: robotics
---
# Model Card for Diffusion Policy / PushT (keypoints)

Diffusion Policy (as per [Diffusion Policy: Visuomotor Policy
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.

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.

## How to Get Started with the Model

Use `python lerobot/scripts/eval.py -p lerobot/diffusion_pusht` to evaluate for 50 episodes with the outputs sent to `outputs/eval`.

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)).

## Training Details

Trained with [LeRobot@cc2f6e7](https://github.com/huggingface/lerobot/tree/cc2f6e74047bd65db0f9705fa602636b625bc28c).

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:

```bash
python lerobot/scripts/train.py \
  hydra.job.name=diffusion_pusht_keypoints \
  hydra.run.dir=outputs/train/2024-07-03/13-52-44_diffusion_pusht_keypoints \
  env=pusht_keypoints \
  policy=diffusion_pusht_keypoints \
  training.save_checkpoint=true \
  training.offline_steps=200000 \
  training.save_freq=20000 \
  training.eval_freq=10000 \
  training.log_freq=50 \
  training.num_workers=4 \
  eval.n_episodes=50 \
  eval.batch_size=50 \
  wandb.enable=true \
  wandb.disable_artifact=true \
  device=cuda \
  use_amp=true
```

The training curves may be found at https://wandb.ai/alexander-soare/lerobot/runs/5z9d8q9q/overview.

This took about 5 hours to train on an Nvida RTX H100.

## Evaluation

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:

- Maximum overlap with target (seen as `eval/avg_max_reward` in the charts above). This ranges in [0, 1].
- Success: whether or not the maximum overlap is at least 95%.

Here are the metrics for 500 episodes worth of evaluation.

Metric|Average over 500 episodes
-|-
Average max. overlap ratio | 0.97
Success rate (%) | 71.0

The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).