metadata

license: apache-2.0
datasets:
  - JayLee131/vqbet_pusht
pipeline_tag: robotics

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.

The model was trained using this command:

python lerobot/scripts/train.py \
  policy=vqbet \
  env=pusht dataset_repo_id=lerobot/pusht \
  wandb.enable=true \
  device=cuda

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

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%.

	Ours
Average max. overlap ratio	0.887
Success rate for 500 episodes (%)	66.0