rex-gym
OpenAI Gym environments and tools for training a 3D-printed quadruped robot (SpotMicro) using reinforcement learning.
What is rex-gym?
Rex-gym is a collection of OpenAI Gym environments and training tools designed to train control policies for an open-source, 3D-printed quadruped robot called Rex. It uses reinforcement learning, specifically Proximal Policy Optimization (PPO), to teach the robot tasks like walking, galloping, and navigating uneven terrain in simulation, with the goal of transferring these policies to the physical robot. The project provides a framework for experimenting with hybrid control strategies that combine pre-defined trajectories with learned adaptations.
Robotics researchers, hobbyists, and developers interested in reinforcement learning for legged locomotion, particularly those working with quadruped robots and seeking to implement sim-to-real transfer. It's also suitable for educators and students exploring robotic control algorithms.
Developers choose Rex-gym because it offers a ready-to-use simulation environment tailored for a popular open-source robot platform (SpotMicro), with implemented PPO training and support for multiple terrains and tasks. Its hybrid policy approach provides flexibility in balancing manual control and autonomous learning, accelerating experimentation and deployment.
Overview
OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
Use Cases
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