| tags: | |
| - lerobot | |
| - robotics | |
| - aloha | |
| - imitation-learning | |
| - act | |
| library_name: pytorch | |
| # ACT Policy for ALOHA Simulation | |
| This policy was trained using Imitation Learning (behavior cloning) on the ALOHA simulation dataset. | |
| ## Model Details | |
| - **Policy Type**: ACT (Action Chunking Transformer) | |
| - **Architecture**: CNN encoder + Transformer decoder | |
| - **State Dimension**: 14 (bimanual robot joints) | |
| - **Action Dimension**: 14 | |
| - **Action Chunk Size**: 50 | |
| - **Hidden Dimension**: 512 | |
| - **Transformer Layers**: 6 | |
| - **Training Epochs**: 6 | |
| - **Best Training Loss**: 0.0008 | |
| ## Training Dataset | |
| - **Dataset**: [nock0912/aloha_sim_cube_dataset](https://huggingface.co/datasets/nock0912/aloha_sim_cube_dataset) | |
| - **Environment**: gym_aloha/AlohaTransferCube-v0 | |
| - **Task**: Transfer cube from one location to another | |
| ## Usage | |
| ```python | |
| import torch | |
| from train_act_policy import SimpleACTPolicy | |
| # Load model | |
| model = SimpleACTPolicy( | |
| state_dim=14, | |
| action_dim=14, | |
| hidden_dim=512, | |
| chunk_size=50, | |
| n_heads=8, | |
| n_layers=6 | |
| ) | |
| model.load_state_dict(torch.load("model.pt")) | |
| model.eval() | |
| # Inference | |
| with torch.no_grad(): | |
| actions = model(images, states) # Returns 50 future actions | |
| ``` | |
| ## Citation | |
| If you use this policy, please cite the LeRobot project. | |