lerobot/vqbet_pusht

Model Card for VQ-BeT/PushT

VQ-BeT (as per Behavior Generation with Latent Actions) 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/vqbet_pusht \
    --policy.type=vqbet \
    --dataset.repo_id=lerobot/pusht \
    --env.type=pusht \
    --seed=100000 \
    --batch_size=64 \
    --steps=250000 \
    --eval_freq=25000 \
    --save_freq=25000 \
    --wandb.enable=true

The training curves may be found at https://wandb.ai/aliberts/lerobot/runs/3i7zs94u. The current model corresponds to the checkpoint at 200k steps.

Model Size

	Number of Parameters
RGB Encoder	11.2M
Remaining VQ-BeT Parts	26.3M

Evaluation

The model was evaluated on the PushT environment from 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	Value
Average max. overlap ratio for 500 episodes	0.895
Success rate for 500 episodes (%)	63.8

The results of each of the individual rollouts may be found in eval_info.json. It was produced after training with this command:

python lerobot/scripts/eval.py \
    --policy.path=outputs/train/vqbet_pusht/checkpoints/200000/pretrained_model \
    --output_dir=outputs/eval/vqbet_pusht/200000 \
    --env.type=pusht \
    --seed=100000 \
    --eval.n_episodes=500 \
    --eval.batch_size=50 \
    --device=cuda \
    --use_amp=false