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
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
This took about 7 hours to train on an Nvida A6000.
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_rewardin 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.
| Ours | |
|---|---|
| Average max. overlap ratio for 500 episodes | 0.887 |
| Success rate for 500 episodes (%) | 66.0 |
The results of each of the individual rollouts may be found in eval_info.json.