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| 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.
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| <div class="video-container">
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| <iframe
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| width="100%"
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| height="415"
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| src="https://www.youtube.com/embed/ft73x0LfGpM"
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| title="LeRobot ACT Tutorial"
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| frameborder="0"
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| allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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| allowfullscreen
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| ></iframe>
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| </div>
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| _Watch this tutorial from the LeRobot team to learn how ACT works: [LeRobot ACT Tutorial](https://www.youtube.com/watch?v=ft73x0LfGpM)_
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| 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.
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| ACT stands out as an excellent starting point for several reasons:
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| - **Fast Training**: Trains in a few hours on a single GPU
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| - **Lightweight**: Only ~80M parameters, making it efficient and easy to work with
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| - **Data Efficient**: Often achieves high success rates with just 50 demonstrations
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| ACT uses a transformer-based architecture with three main components:
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| 1. **Vision Backbone**: ResNet-18 processes images from multiple camera viewpoints
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| 2. **Transformer Encoder**: Synthesizes information from camera features, joint positions, and a learned latent variable
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| 3. **Transformer Decoder**: Generates coherent action sequences using cross-attention
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| The policy takes as input:
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| - Multiple RGB images (e.g., from wrist cameras, front/top cameras)
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| - Current robot joint positions
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| - A latent style variable `z` (learned during training, set to zero during inference)
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| And outputs a chunk of `k` future action sequences.
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| 1. Install LeRobot by following our [Installation Guide](./installation).
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| 2. ACT is included in the base LeRobot installation, so no additional dependencies are needed!
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| ACT works seamlessly with the standard LeRobot training pipeline. Here's a complete example for training ACT on your dataset:
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| ```bash
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| lerobot-train \
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| --dataset.repo_id=${HF_USER}/your_dataset \
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| --policy.type=act \
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| --output_dir=outputs/train/act_your_dataset \
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| --job_name=act_your_dataset \
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| --policy.device=cuda \
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| --wandb.enable=true \
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| --policy.repo_id=${HF_USER}/act_policy
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| ```
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|
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| ### Training Tips
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| 1. **Start with defaults**: ACT's default hyperparameters work well for most tasks
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| 2. **Training duration**: Expect a few hours for 100k training steps on a single GPU
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| 3. **Batch size**: Start with batch size 8 and adjust based on your GPU memory
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| 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).
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| ## Evaluating ACT
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| 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:
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|
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| ```bash
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| lerobot-record \
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| --robot.type=so100_follower \
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| --robot.port=/dev/ttyACM0 \
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| --robot.id=my_robot \
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| --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
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| --display_data=true \
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| --dataset.repo_id=${HF_USER}/eval_act_your_dataset \
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| --dataset.num_episodes=10 \
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| --dataset.single_task="Your task description" \
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| --policy.path=${HF_USER}/act_policy
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| ```
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| |
