amp-rsl-rl 2.2.1

Adversarial Motion Prior (AMP) reinforcement learning extension for PPO based on RSL-RL.

AMP-RSL-RL

AMP-RSL-RL is a reinforcement learning library that extends the Proximal Policy Optimization (PPO) implementation of RSL-RL to incorporate Adversarial Motion Priors (AMP). This framework enables humanoid agents to learn motor skills from motion capture data using adversarial imitation learning techniques.

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📦 Installation

The repository is available on PyPI under the package name amp-rl-rsl. You can install it directly using pip:

pip install amp-rsl-rl

Alternatively, if you prefer to clone the repository and install it locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/gbionics/amp_rsl_rl.git
   cd amp_rsl_rl
   

2. Install the package:

   pip install .
   

For editable/development mode:

pip install -e .

If you want to run the examples, please install with:

pip install .[examples]

The required dependencies include:

• numpy
• scipy
• torch
• rsl-rl-lib

These will be automatically installed via pip.

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📂 Project Structure

amp_rsl_rl/
│
├── algorithms/        # AMP and PPO implementations
├── networks/          # Neural networks for policy and discriminator
├── runners/           # Training and evaluation routines
├── storage/           # Replay buffer for experience collection
├── utils/             # Dataset loaders and motion tools

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📁 Dataset Structure

The AMP-RSL-RL framework expects motion capture datasets in .npy format. Each .npy file must contain a Python dictionary with the following keys:

• joints_list: List[str]
  A list of joint names. These should correspond to the joint order expected by the agent.

• joint_positions: List[np.ndarray]
  A list where each element is a NumPy array representing the joint positions at a frame. All arrays should have the same shape (N,), where N is the number of joints.

• root_position: List[np.ndarray]
  A list of 3D vectors representing the position of the base (root) of the agent in world coordinates for each frame.

• root_quaternion: List[np.ndarray]
  A list of unit quaternions in xyzw format (SciPy convention), representing the base orientation of the agent for each frame.

• fps: float
  The number of frames per second in the original dataset. This is used to resample the data to match the simulator's timestep.

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� Symmetry Augmentation

AMP-RSL-RL now exposes the symmetry-aware data augmentation and mirror-loss hooks from RSL-RL. The implementation follows the design described in:

Mittal, M., Rudin, N., Klemm, V., Allshire, A., & Hutter, M. (2024).
Symmetry Considerations for Learning Task Symmetric Robot Policies. In IEEE International Conference on Robotics and Automation (ICRA).
https://doi.org/10.1109/ICRA57147.2024.10611493 Symmetry augmentation can be enabled through the symmetry_cfg section of the algorithm configuration, providing both minibatch augmentation and optional mirror-loss regularisation for the policy update. AMP-specific components (the discriminator and expert/policy motion buffers) are augmented using the same configuration so that style rewards and adversarial training remain consistent with their symmetric counterparts.

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Example

Here’s an example of how the structure might look when loaded in Python:

{
    "joints_list": ["hip", "knee", "ankle"],
    "joint_positions": [np.array([0.1, -0.2, 0.3]), np.array([0.11, -0.21, 0.31]), ...],
    "root_position": [np.array([0.0, 0.0, 1.0]), np.array([0.01, 0.0, 1.0]), ...],
    "root_quaternion": [np.array([0.0, 0.0, 0.0, 1.0]), np.array([0.0, 0.0, 0.1, 0.99]), ...],
    "fps": 120.0
}

All lists must have the same number of entries (i.e. one per frame). The dataset should represent smooth motion captured over time.

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📚 Supported Dataset

For a ready-to-use motion capture dataset, you can use the AMP Dataset on Hugging Face. This dataset is curated to work seamlessly with the AMP-RSL-RL framework.

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🧑‍💻 Authors

• Giulio Romualdi – @GiulioRomualdi
• Giuseppe L'Erario – @Giulero

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📄 License

BSD 3-Clause License © 2025 Istituto Italiano di Tecnologia