humanoid gym

Built on Isaac Gym and IsaacSim from NVIDIA, it provides GPU-accelerated physics simulation at the thousands-of-environments scale needed to train effective RL policies for complex high-degree-of-freedom systems like humanoid robots.

The framework includes reward shaping utilities, reference motion tracking infrastructure, and standardized evaluation protocols that simplify the research and development cycle for humanoid robot learning.

The project addresses the particular challenges of humanoid locomotion training: reward engineering for stable bipedal gait, handling contact dynamics during footstep transitions, transferring policies from simulation to real hardware (the sim-to-real gap), and curriculum learning strategies that build up from simpler motions to complex dynamic behaviors.

Pre-configured environments for common humanoid research taskswalking, running, stair climbing, and whole-body manipulationgive researchers a clean starting point without requiring them to build simulation infrastructure from scratch.

Robotics researchers studying legged locomotion, reinforcement learning teams at humanoid robot companies, and academic labs working on embodied AI use Humanoid Gym as their primary training infrastructure for humanoid control policies.

The GPU-parallelized simulation enables experiments at the scale required for modern RL algorithmsthousands of parallel rollouts running simultaneouslywhich would be impractical with CPU-based simulation.

As investment in humanoid robotics has grown significantly, frameworks like Humanoid Gym have become essential infrastructure for the research community developing the learned behaviors that make capable humanoid robots possible.