File size: 1,704 Bytes
bcc39cb b756961 bcc39cb |
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 54 55 56 57 58 59 60 |
---
library_name: lerobot
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- robotics
- dot
license: apache-2.0
datasets:
- lerobot/pusht
pipeline_tag: robotics
---
# Model Card for "Decoder Only Transformer (DOT) Policy" for PushT images dataset
Read more about the model and implementation details in the [DOT Policy repository](https://github.com/IliaLarchenko/dot_policy).
This model is trained using the [LeRobot library](https://huggingface.co/lerobot) and achieves state-of-the-art results on behavior cloning on the PushT images dataset. It achieves a 74.2% success rate (and 0.936 average max reward) vs. ~69% for the previous state-of-the-art model (Diffusion and VQ-BET perform the same).
This result is achieved without the checkpoint selection and is easy to reproduce.
You can use this model by installing LeRobot from [this branch](https://github.com/IliaLarchenko/lerobot/tree/dot)
To train the model:
```bash
python lerobot/scripts/train.py \
--policy.type=dot \
--dataset.repo_id=lerobot/pusht \
--env.type=pusht \
--env.task=PushT-v0 \
--output_dir=outputs/train/pusht_images \
--batch_size=24 \
--log_freq=1000 \
--eval_freq=10000 \
--save_freq=50000 \
--offline.steps=1000000 \
--seed=100000 \
--wandb.enable=true \
--num_workers=24 \
--use_amp=true \
--device=cuda \
--policy.return_every_n=2
```
To evaluate the model:
```bash
python lerobot/scripts/eval.py \
--policy.path=jadechoghari/dot_pusht_images \
--env.type=pusht \
--env.task=PushT-v0 \
--eval.n_episodes=1000 \
--eval.batch_size=100 \
--seed=1000000
```
Model size:
- Total parameters: 14.1m
- Trainable parameters: 2.9m |