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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_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_new_config)
To train the model:
```bash
python lerobot/scripts/train.py \
--policy.type=dot \
--dataset.repo_id=lerobot/aloha_sim_insertion_human \
--env.type=aloha \
--env.task=AlohaInsertion-v0 \
--env.episode_length=500 \
--output_dir=outputs/train/pusht_aloha_insert \
--batch_size=24 \
--log_freq=1000 \
--eval_freq=10000 \
--save_freq=10000 \
--offline.steps=100000 \
--seed=100000 \
--wandb.enable=true \
--num_workers=24 \
--use_amp=true \
--device=cuda \
--policy.optimizer_lr=0.00003 \
--policy.optimizer_min_lr=0.00001 \
--policy.optimizer_lr_cycle_steps=100000 \
--policy.train_horizon=150 \
--policy.inference_horizon=100 \
--policy.lookback_obs_steps=30 \
--policy.lookback_aug=5 \
--policy.rescale_shape="[480,640]" \
--policy.alpha=0.98 \
--policy.train_alpha=0.99
```
To evaluate the model:
```bash
python lerobot/scripts/eval.py \
--policy.path=IliaLarchenko/dot_bimanual_insert \
--env.type=aloha \
--env.task=AlohaInsertion-v0 \
--env.episode_length=500 \
--eval.n_episodes=1000 \
--eval.batch_size=100 \
--seed=1000000
```
Model size:
- Total parameters: 14.1m
- Trainable parameters: 2.9m
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