đ Model Card for VQ-BeT/PushT
VQ-BeT is trained for the PushT
environment, offering advanced solutions for robotics tasks.
đ Quick Start
See the LeRobot library (particularly the evaluation script) for instructions on how to load and evaluate this model.
⨠Features
- VQ-BeT Policy: Based on the VQ-BeT architecture, trained for the
PushT
environment.
- Robotics Application: Specifically designed for robotics tasks, with a focus on the
PushT
scenario.
đĻ Installation
No specific installation steps are provided in the original document.
đģ Usage Examples
Basic Usage
To load and evaluate the model, refer to the LeRobot library and the evaluation script.
đ Documentation
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
Property |
Details |
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
đ License
This project is licensed under the Apache-2.0 license.