diffusion pusht EU AI Act Compliance Profile

Model Card for Diffusion Policy / PushT

Diffusion Policy (as per Diffusion Policy: Visuomotor Policy Learning via Action Diffusion) trained for the PushT environment from gym-pusht.

How to Get Started with the Model

See the LeRobot library (particularly the evaluation script) for instructions on how to load and evaluate this model.

Training Details

Trained with LeRobot@3c0a209.

The model was trained using LeRobot's training script and with the pusht dataset, using this command:

python lerobot/scripts/train.py \
    --output_dir=outputs/train/diffusion_pusht \
    --policy.type=diffusion \
    --dataset.repo_id=lerobot/pusht \
    --seed=100000 \
    --env.type=pusht \
    --batch_size=64 \
    --steps=200000 \
    --eval_freq=25000 \
    --save_freq=25000 \
    --wandb.enable=true

The training curves may be found at https://wandb.ai/aliberts/lerobot/runs/s7elvf4r. The current model corresponds to the checkpoint at 175k steps.

Evaluation

The model was evaluated on the PushT environment from gym-pusht and compared to a similar model trained with the original Diffusion Policy code. 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. The "Theirs" column is for an equivalent model trained on the original

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