# ACT (Action Chunking with Transformers) ACT is a **lightweight and efficient policy for imitation learning**, especially well-suited for fine-grained manipulation tasks. It's the **first model we recommend when you're starting out** with LeRobot due to its fast training time, low computational requirements, and strong performance.
_Watch this tutorial from the LeRobot team to learn how ACT works: [LeRobot ACT Tutorial](https://www.youtube.com/watch?v=ft73x0LfGpM)_ ## Model Overview Action Chunking with Transformers (ACT) was introduced in the paper [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://arxiv.org/abs/2304.13705) by Zhao et al. The policy was designed to enable precise, contact-rich manipulation tasks using affordable hardware and minimal demonstration data. ### Why ACT is Great for Beginners ACT stands out as an excellent starting point for several reasons: - **Fast Training**: Trains in a few hours on a single GPU - **Lightweight**: Only ~80M parameters, making it efficient and easy to work with - **Data Efficient**: Often achieves high success rates with just 50 demonstrations ### Architecture ACT uses a transformer-based architecture with three main components: 1. **Vision Backbone**: ResNet-18 processes images from multiple camera viewpoints 2. **Transformer Encoder**: Synthesizes information from camera features, joint positions, and a learned latent variable 3. **Transformer Decoder**: Generates coherent action sequences using cross-attention The policy takes as input: - Multiple RGB images (e.g., from wrist cameras, front/top cameras) - Current robot joint positions - A latent style variable `z` (learned during training, set to zero during inference) And outputs a chunk of `k` future action sequences. ## Installation Requirements 1. Install LeRobot by following our [Installation Guide](./installation). 2. ACT is included in the base LeRobot installation, so no additional dependencies are needed! ## Training ACT ACT works seamlessly with the standard LeRobot training pipeline. Here's a complete example for training ACT on your dataset: ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/your_dataset \ --policy.type=act \ --output_dir=outputs/train/act_your_dataset \ --job_name=act_your_dataset \ --policy.device=cuda \ --wandb.enable=true \ --policy.repo_id=${HF_USER}/act_policy ``` ### Training Tips 1. **Start with defaults**: ACT's default hyperparameters work well for most tasks 2. **Training duration**: Expect a few hours for 100k training steps on a single GPU 3. **Batch size**: Start with batch size 8 and adjust based on your GPU memory ### Train using Google Colab If your local computer doesn't have a powerful GPU, you can utilize Google Colab to train your model by following the [ACT training notebook](./notebooks#training-act). ## Evaluating ACT Once training is complete, you can evaluate your ACT policy using the `lerobot-record` command with your trained policy. This will run inference and record evaluation episodes: ```bash lerobot-record \ --robot.type=so100_follower \ --robot.port=/dev/ttyACM0 \ --robot.id=my_robot \ --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \ --display_data=true \ --dataset.repo_id=${HF_USER}/eval_act_your_dataset \ --dataset.num_episodes=10 \ --dataset.single_task="Your task description" \ --policy.path=${HF_USER}/act_policy ```