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--- |
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library_name: lerobot |
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tags: |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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- robotics |
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- dot |
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license: apache-2.0 |
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datasets: |
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- lerobot/pusht |
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pipeline_tag: robotics |
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--- |
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# Model Card for "Decoder Only Transformer (DOT) Policy" for PushT images dataset |
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Read more about the model and implementation details in the [DOT Policy repository](https://github.com/IliaLarchenko/dot_policy). |
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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). |
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This result is achieved without the checkpoint selection and is easy to reproduce. |
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You can use this model by installing LeRobot from [this branch](https://github.com/IliaLarchenko/lerobot/tree/dot) |
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To train the model: |
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```bash |
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python lerobot/scripts/train.py \ |
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--policy.type=dot \ |
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--dataset.repo_id=lerobot/pusht \ |
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--env.type=pusht \ |
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--env.task=PushT-v0 \ |
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--output_dir=outputs/train/pusht_images \ |
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--batch_size=24 \ |
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--log_freq=1000 \ |
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--eval_freq=10000 \ |
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--save_freq=50000 \ |
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--offline.steps=1000000 \ |
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--seed=100000 \ |
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--wandb.enable=true \ |
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--num_workers=24 \ |
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--use_amp=true \ |
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--device=cuda \ |
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--policy.return_every_n=2 |
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``` |
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To evaluate the model: |
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```bash |
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python lerobot/scripts/eval.py \ |
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--policy.path=IliaLarchenko/dot_pusht_images \ |
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--env.type=pusht \ |
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--env.task=PushT-v0 \ |
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--eval.n_episodes=1000 \ |
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--eval.batch_size=100 \ |
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--seed=1000000 |
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``` |
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Model size: |
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- Total parameters: 14.1m |
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- Trainable parameters: 2.9m |
