|
--- |
|
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](https://arxiv.org/abs/2403.03181)) trained for the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht). |
|
|
|
## How to Get Started with the Model |
|
|
|
See the [LeRobot library](https://github.com/huggingface/lerobot) (particularly the [evaluation script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py)) for instructions on how to load and evaluate this model. |
|
|
|
## Training Details |
|
|
|
Trained with [LeRobot@342f429](https://github.com/huggingface/lerobot/tree/342f429f1c321a2b4501c3007b1dacba7244b469). |
|
|
|
The model was trained using this command: |
|
|
|
```bash |
|
python lerobot/scripts/train.py \ |
|
policy=vqbet \ |
|
env=pusht dataset_repo_id=lerobot/pusht \ |
|
wandb.enable=true \ |
|
device=cuda |
|
``` |
|
|
|
The training curves may be found at https://wandb.ai/jaylee0301/lerobot/runs/9r0ndphr?nw=nwuserjaylee0301. |
|
|
|
Training VQ-BeT on PushT took about 7-8 hours to train on an Nvida A6000. |
|
|
|
## Model Size |
|
|
|
<blank>|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](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. |
|
|
|
<blank>|Ours |
|
-|- |
|
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](eval_info.json). |
