---
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.