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README.md

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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

replay.mp4

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