IliaLarchenko/dot_pusht_keypoints

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.

This model is trained using the LeRobot library and achieves state-of-the-art results on behavior cloning on the PushT keypoints dataset. It achieves 84.5% success rate (and 0.964 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 result is achieved without the checkpoint selection. If you are interested in an even better model with a success rate of ~94% (but harder to reproduce as it requires some parameters tuning and checkpoint selection), please refer to this model

You can use this model by installing LeRobot from this branch

To train the model:

python lerobot/scripts/train.py policy=dot_pusht_keypoints env=pusht env.gym.obs_type=environment_state_agent_pos

To evaluate the model:

python lerobot/scripts/eval.py -p IliaLarchenko/dot_pusht_keypoints eval.n_episodes=1000 eval.batch_size=100 seed=1000000

Model size:

• Total parameters: 2.1m
• Trainable parameters: 2.1m