---
library_name: lerobot
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- robotics
- dot
license: apache-2.0
datasets:
- lerobot/pusht_keypoints
pipeline_tag: robotics
---

# Model Card for "Decoder Only Transformer (DOT) Policy" for PushT keypoints 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 keypoints dataset. It achieves 94% success rate (and 0.985 average max reward) vs. ~78% for the previous state-of-the-art model or 69% that I managed to reproduce using VQ-BET implementation in LeRobot.

This is the best checkpoint for the model. These results are achievable assuming we have reliable validation and can select the best checkpoint based on the validation results (not always the case in robotics). If you are interested in more stable and reproducible results achievable without checkpoint selection, please refer to https://huggingface.co/IliaLarchenko/dot_pusht_keypoints

You can use this model by installing LeRobot from [this branch](https://github.com/IliaLarchenko/lerobot/tree/dot_new_config)

To train the model:

```bash
python lerobot/scripts/train.py \
    --policy.type=dot \
    --dataset.repo_id=lerobot/pusht_keypoints \
    --env.type=pusht \
    --env.task=PushT-v0 \
    --output_dir=outputs/train/pusht_keyponts \
    --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 \
    --policy.train_horizon=30 \
    --policy.inference_horizon=30
```

To evaluate the model:

```bash
python lerobot/scripts/eval.py \
    --policy.path=IliaLarchenko/dot_pusht_keypoints_best \
    --env.type=pusht \
    --env.task=PushT-v0 \
    --eval.n_episodes=1000 \
    --eval.batch_size=100 \
    --env.obs_type=environment_state_agent_pos \
    --seed=1000000
```

Model size:
- Total parameters: 2.1m
- Trainable parameters: 2.1m