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===PhysX===
PhysX is a physics engine by NVIDIA used primarily for video games and real-time simulations. It supports rigid body dynamics, cloth simulation, and particle effects, enhancing realism and interactivity in 3D environments.
===MujocoMuJoCo===
MuJoCo (Multi-Joint dynamics with Contact) is a physics engine designed for research in robotics and biomechanics. It's known for its speed, accuracy, and ease of use, making it popular for simulating complex systems with robotics and articulated structures.
=== Isaac Sim Integration with Isaac Gym ===
Note: Isaac Gym is now deprecated. NVIDIA now forwards users to their improved toolkit, Isaac Lab, also built on top of Isaac Sim.<br>
Isaac Sim works alongside Isaac Gym, NVIDIA’s tool for large-scale training with reinforcement learning. While Isaac Sim focuses on detailed simulations, Isaac Gym is great for efficient training. Together, they offer a comprehensive solution for developing and improving robotics applications.
Popular training frameworks are listed here with example applications.
===[https://isaac-sim.github.io/IsaacLab/index.html Isaac Lab]===
Isaac Lab is NVIDIA's modular framework for robot learning that aims to simplify common workflows in robotics research, part of the Isaac SDK. It is the successor to Isaac Gym.
===[https://github.com/NVIDIA-Omniverse/IsaacGymEnvs Isaac Gym]===
Note: Isaac Gym is now deprecated in favor of Isaac Lab. Isaac Gym is NVIDIA's robotics simulation tool, part of the Isaac SDK. It leverages GPU acceleration to enable the simulation of thousands of robot bodies simultaneously, making it highly efficient for training machine learning models in robotics. It's designed to streamline robotics applications, focusing on reinforcement learning in a virtual environment.
===[https://gymnasium.farama.org/ Gymnasium]===
Gymnasium is an open-source toolkit for developing and comparing reinforcement learning algorithms. Originally developed by OpenAI as "Gym," it provides a standardized set of environments (like Atari games, robotic simulations, etc.) to test and benchmark AI algorithms. It's widely used in the AI research community to foster innovation and replication in RL studies.