xbpeng/DeepMimic
DeepMimic
DeepMimic is a deep reinforcement learning framework and physics-based motion imitation tool designed to teach simulated characters and robots to reproduce human movements. It provides a pipeline for integrating motion capture data into physics simulations to train agents that can mimic complex physical skills.
The system utilizes the PyBullet simulation environment to execute motion policies and visualize character interactions in real time. It includes a motion capture integration pipeline that imports and processes animation sequences to serve as reference targets for imitation learning agents.
The framework covers a broad set of capabilities including actor-critic policy gradient optimization, parallelized worker sampling for experience collection, and reward-based imitation learning. It also provides tools for data management, motion policy training, and real-time simulation interaction.
Features
- Reinforcement Learning - A framework for training simulated characters to mimic human movement using deep reinforcement learning.
- Actor-Critic Architectures - Implements an actor-critic architecture to optimize neural network policies for motion imitation.
- Physics-Based Motion Imitation Tools - Provides a specialized tool for teaching virtual agents to reproduce human movements via physics simulation.
- Distance-Based - Calculates rewards based on the distance between simulated joint angles and reference motion capture data.
- Reinforcement Learning Training - Trains simulated characters to imitate specific skills by processing motion capture data through reinforcement learning.
- Imitation and Reinforcement Learning Toolkits - Offers a toolkit combining imitation and reinforcement learning to train robot policies in simulation.
- Joint Torque Control - Provides physics-based control of characters by applying joint torques to match target poses.
- Motion Capture Integration - Provides a pipeline to import and integrate motion capture data as reference targets for simulations.
- Parallel Experience Collection - Uses parallel experience collection to accelerate the gathering of transitions from multiple concurrent simulations.
- State-Space Feature Vectors - Feeds the neural network a concatenated array of joint positions, velocities, and reference motion frames.
- Robotics Simulators - Develops and tests control policies that allow simulated agents to execute complex physical skills autonomously.
- Policy Execution Visualization - Executes pre-trained policies or plays back motion capture clips to visualize character performance.
- Physics Simulation Environments - Utilizes the PyBullet environment to execute motion policies and visualize character interactions in real time.
- Physics-Based Character Animation - Example-guided reinforcement learning for physics-based character skills.
