leggedrobotics/legged_gym
Legged Gym
Legged Gym is a high-performance simulation platform and toolkit engineered for training autonomous robotic agents in complex, physics-based environments. It provides a comprehensive framework for developing legged locomotion control policies, enabling robots to learn movement strategies for navigating uneven terrain and managing physical disturbances through reinforcement learning.
The platform distinguishes itself by utilizing hardware-accelerated physics and headless execution to maximize computational throughput during training. It incorporates a domain randomization pipeline that injects stochastic variations into physical parameters, such as friction and mass, to bridge the gap between simulated training and physical hardware deployment. By decoupling policy inference from physics state updates, the system allows for flexible evaluation and deployment of learned controllers.
The framework supports a modular architecture for defining custom robotic assets, reward functions, and task parameters through object-oriented inheritance. This structure facilitates the creation of scalable simulation environments where robot sensor data and environmental observations are encoded into contiguous memory buffers for direct consumption by training pipelines.
The repository includes documentation and configuration files to support the registration of custom environments and the execution of iterative reinforcement learning training loops.
Features
- Robot Learning Platforms - Provides a collection of simulation environments designed to teach robots how to walk and navigate across challenging terrain using reinforcement learning.
- Reinforcement Learning Environments - Trains autonomous agents to perform complex physical tasks by simulating interactions within high-performance physics environments.
- Reinforcement Learning Training Loops - Provides reinforcement learning training loops to optimize robotic agent policies through iterative simulation, hardware configuration, and performance evaluation.
- Locomotion Policies - Develops movement strategies for walking and running robots by training policies that handle uneven terrain and physical disturbances.
- Parallelized Robotics Simulations - Provides a high-performance, parallelized simulation environment specifically designed for training legged robotic agents through reinforcement learning.
- Domain Randomizations - Injects stochastic variations into physical parameters during simulation to bridge the reality gap between virtual training and physical hardware deployment.
- GPU-Accelerated Robot Simulators - Executes high-fidelity rigid body dynamics directly on the graphics processor to enable massive parallelization of robotic training environments.
- Sim-to-Real Transfer - Prepares robotic control policies in simulated environments with randomized dynamics to ensure successful deployment on physical hardware.
- Training-Serving Environment Decoupling - Separates neural network inference logic from physics engine state updates to allow for flexible evaluation and deployment of learned controllers.
- Imitation and Reinforcement Learning Toolkits - Provides a modular system for defining reward functions, robot assets, and training parameters to develop robust movement strategies for legged platforms.
- Training Configurations - Offers training task configuration using modular classes to define robot-specific assets, reward functions, and parameters for consistent learning outcomes.
- Domain Randomization Pipelines - Implements a domain randomization pipeline that varies physical parameters like friction and mass to bridge the gap between simulation and physical hardware.
- Robotic Physics and Sensor Simulators - Models complex physical interactions and actuator dynamics for training autonomous robotic agents in a high-performance simulation environment.
- Robotics Simulators - Builds scalable and modular simulation setups to test and refine the physical behavior of robots under diverse conditions.
- Headless Execution Environments - Runs multiple concurrent environment instances without graphical rendering to maximize computational throughput during intensive reinforcement learning training cycles.
- Modular Task Configurations - Uses object-oriented inheritance to define reward functions and environment parameters, allowing for rapid iteration across diverse robotic platforms and locomotion goals.
