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IliaLarchenko/
dot_pusht_images

Name: dot_pusht_images
Availability: InStock

lerobot

safetensors

model_hub_mixin

pytorch_model_hub_mixin

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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.

This model is trained using the LeRobot library 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

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=IliaLarchenko/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