Biped Locomotion: Deep Phase Motion Generation Framework

Overview

Biped Locomotion is a robot-agnostic framework that adapts the DeepPhase motion generation system (Starke et al., 2022) for practical robotics applications. This implementation transforms the original animation-focused approach into a flexible framework for generating fluid, natural motions for various robot platforms.

Key contributions of this repo include:

• Pure Python-based data processing pipeline (removing Unity dependency)
• Robot-agnostic architecture supporting various robot configurations
• Streamlined data processing and training workflow
• Flexible deployment options for robotics applications

Technical Architecture

The framework consists of two main components:

1. Periodic Autoencoder (PAE)

The PAE learns to encode motion data into a compact, periodic representation:

• Input : Joint states (positions and velocities) with window-based normalization
• Output : Periodic phase parameters (shift, frequency, amplitude)

2. Mode-Adaptive Neural Network (MANN)

The MANN uses the phase representation to predict motion:

• Input : Command velocities, robot state, and phase space representation
• Output : Next robot state, joint positions/velocities, contact states, and phase updates

Installation

Prerequisites

• Python 3.9
• CUDA-compatible GPU (recommended)
• Git LFS (for managing large data files)

Clone the Repository

# Install Git LFS first if you haven't
apt-get install git-lfs  # Ubuntu/Debian
# OR
brew install git-lfs  # macOS

# Clone and initialize LFS
git clone https://github.com/ethanmclark1/biped_locomotion.git
cd biped_locomotion
git lfs install
git lfs pull

Environment Setup

# Create and activate conda environment
conda env create -f environment.yml
conda activate biped_locomotion

# Verify installation
python -c "import torch; import numpy as np; import quaternion; import scipy"

Directory Structure

The repository is organized as follows:

biped_locomotion/
├── data/                # Motion capture data (LFS tracked)
├── scripts/             # Core implementation
│   ├── mann/            # Mode-Adaptive Neural Network
│   ├── pae/             # Periodic Autoencoder
│   ├── test/            # Test utilities
│   └── utils/           # Helper functions
├── ckpt/                # Model checkpoints (created during training)
└── img/                 # Output visualizations (created during training)

Usage

Important : All scripts should be run from the project root directory to avoid import errors!

Data Preparation

Place your motion data in the biped_locomotion/data/sequence_X/ directories with:

• walking_joint_states.npy: Joint angles in degrees
• walking_root_states.npy: Root position, orientation and velocities
• walking_foot_contacts.npy: Contact states

Training Pipeline

1. Train the Periodic Autoencoder (PAE)

# Run from project root
python -m biped_locomotion.scripts.pae.trainer

This learns to encode joint motions into periodic phase parameters.

2. Train the Mode-Adaptive Neural Network (MANN)

# Run from project root 
python -m biped_locomotion.scripts.mann.trainer

This learns to predict motion based on command inputs and phase representation.

Inference

To run inference and generate motion:

python -m biped_locomotion.scripts.mann.inference

Configuration

Modify biped_locomotion/scripts/config.yaml to adjust:

• Network architecture parameters
• Training hyperparameters
• Data processing settings

Troubleshooting

Import Errors

If you encounter ModuleNotFoundError: No module named 'biped_locomotion', ensure you're running scripts from the project root:

# Correct way to run scripts
cd /path/to/biped_locomotion
python -m biped_locomotion.scripts.pae.trainer

CUDA Issues

If you encounter CUDA errors, verify your PyTorch installation matches your CUDA version:

python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}, Version: {torch.version.cuda}')"

Model Configuration

PAE Configuration

• phase_channels: Number of phase channels (default: 8)
• intermediate_channels: Dimension of latent space (default: 40)
• full_joint_state: Whether to use both positions and velocities (default: False)

MANN Configuration

• gating_hidden: Hidden layer size for gating network
• main_hidden: Hidden layer size for main network
• n_experts: Number of expert networks in mixture

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the Apache 2.0 License - see LICENSE file for details.

Citation

If you use this implementation in your research, please cite the original DeepPhase paper:

@inproceedings{starke2022deepphase,
  title={DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds},
  author={Starke, Sebastian and Mason, Ian and Komura, Taku},
  booktitle={ACM SIGGRAPH 2022 Conference Proceedings},
  year={2022},
  organization={ACM}
}