Overview
NVIDIA Isaac Gym was a physics simulation environment designed for reinforcement learning research. It leveraged GPU acceleration to provide high-throughput simulation for training robots and agents. Its architecture supports importing URDF and MJCF files, enabling the simulation of complex robotic systems. Automatic convex decomposition converted 3D meshes for efficient physical simulation. A tensor API facilitated environment state evaluation and action application. Domain randomization of physics parameters was supported to improve the robustness of trained policies. While now deprecated, its features are succeeded by Isaac Lab, including SDF collisions, gyroscopic forces, and customized contact offsets. The Isaac Gym Preview 4 release aligned its PhysX implementation with Omniverse Isaac Sim 2022.1, simplifying migration for reinforcement learning workloads.