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---
library_name: lerobot
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- robotics
- dot
license: apache-2.0
datasets:
- lerobot/aloha_sim_transfer_cube_human
pipeline_tag: robotics
---
# Model Card for "Decoder Only Transformer (DOT) Policy" for ALOHA cube transfer 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 92.6% success rate vs. 83% for the previous state-of-the-art model (ACT). (Note: it looks like the LeRobot implementation is not deterministic of environment makes it easier than the original problem, I am comparing it with https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human).
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/aloha_sim_transfer_cube_human \
--env.type=aloha \
--env.task=AlohaTransferCube-v0 \
--output_dir=outputs/train/pusht_aloha_transfer_cube \
--batch_size=24 \
--log_freq=1000 \
--eval_freq=5000 \
--save_freq=5000 \
--offline.steps=100000 \
--seed=100000 \
--wandb.enable=true \
--num_workers=24 \
--use_amp=true \
--device=cuda \
--policy.optimizer_lr=0.0001 \
--policy.optimizer_min_lr=0.0001 \
--policy.optimizer_lr_cycle_steps=100000 \
--policy.train_horizon=75 \
--policy.inference_horizon=50 \
--policy.lookback_obs_steps=20 \
--policy.lookback_aug=5 \
--policy.rescale_shape="[480,640]" \
--policy.alpha=0.98 \
--policy.train_alpha=0.99 \
--wandb.project=transfer_cube
```
To evaluate the model:
```bash
python lerobot/scripts/eval.py \
--policy.path=IliaLarchenko/dot_transfer_cube \
--env.type=aloha \
--env.task=AlohaTransferCube-v0 \
--eval.n_episodes=1000 \
--eval.batch_size=100 \
--seed=1000000
```
Model size:
- Total parameters: 14.1m
- Trainable parameters: 2.9m
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