| library_name: lerobot | |
| tags: | |
| - model_hub_mixin | |
| - pytorch_model_hub_mixin | |
| - robotics | |
| - dot | |
| license: apache-2.0 | |
| datasets: | |
| - lerobot/aloha_sim_insertion_human | |
| pipeline_tag: robotics | |
| # Model Card for "Decoder Only Transformer (DOT) Policy" for ALOHA bimanual insert problem | |
| 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 ALOHA bimanual insert dataset. It achieves 29.6% success rate vs. 21% for the previous state-of-the-art model (ACT). | |
| 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=dot_insert env=aloha env.episode_length=500 | |
| ``` | |
| To evaluate the model: | |
| ```bash | |
| python lerobot/scripts/eval.py -p IliaLarchenko/dot_bimanual_insert eval.n_episodes=1000 eval.batch_size=100 seed=1000000 | |
| ``` | |
| Model size: | |
| - Total parameters: 14.1m | |
| - Trainable parameters: 2.9m | |