File size: 2,205 Bytes
36ff285 2d44f2b 36ff285 2d44f2b 36ff285 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
---
license: apache-2.0
datasets:
- lerobot/pusht
pipeline_tag: robotics
---
# Model Card for Transformer based Diffusion Policy / PushT
Transformer based Diffusion Policy (as per [Diffusion Policy: Visuomotor Policy
Learning via Action Diffusion](https://arxiv.org/abs/2303.04137)) 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
The model was trained using [LeRobot's training script](https://github.com/huggingface/lerobot/blob/d747195c5733c4f68d4bfbe62632d6fc1b605712/lerobot/scripts/train.py) and with the [pusht](https://huggingface.co/datasets/lerobot/pusht/tree/v1.3) dataset, using this command:
```bash
python lerobot/scripts/train.py \
hydra.run.dir=outputs/train/diffusion_pusht \
hydra.job.name=diffusion_pusht \
policy=diffusion training.save_model=true \
env=pusht \
env.task=PushT-v0 \
dataset_repo_id=lerobot/pusht \
training.save_freq=25000 \
training.eval_freq=10000 \
wandb.enable=true \
device=cuda
```
The training and eval curves may be found at https://api.wandb.ai/links/none7/rd2trav7
This took about 6 hours to train on an Nvida Tesla P100 GPU.
## Evaluation
The model was evaluated on the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht) and compared to a similar model trained with the original [Diffusion Policy code](https://github.com/real-stanford/diffusion_policy). 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|Theirs
-|-|-
Average max. overlap ratio | 0.000 | 0.000
Success rate for 500 episodes (%) | 0.00 | 0.00
The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json). |