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Isaac Lab is a unified and modular framework for robot learning that aims to simplify common workflows in robotics research (such as reinforcement learning, learning from demonstrations, and motion planning). It is built upon NVIDIA Isaac Sim to leverage the latest simulation capabilities for photo-realistic scenes, and fast and efficient simulation. The core objectives of the framework are: Modularity: Easily customize and add new environments, robots, and sensors. Agility: Adapt to the changing needs of the community. Openness: Remain open-sourced to allow the community to contribute and extend the framework. Battery-included: Include a number of environments, sensors, and tasks that are ready to use. Key features available in Isaac Lab include fast and accurate physics simulation provided by PhysX, tiled rendering APIs for vectorized rendering, domain randomization for improving robustness and adaptability, and support for running in the cloud. Additionally, Isaac Lab provides over 26 environments, and we are actively working on adding more environments to the list. These include classic control tasks, fixed-arm and dexterous manipulation tasks, legged locomotion tasks, and navigation tasks. A complete list is available in the environments section. The framework also includes over 16 robots. If you are looking to add a new robot, please refer to the How-to Guides section. The current list of robots includes: Classic Cartpole, Humanoid, Ant Fixed-Arm and Hands: UR10, Franka, Allegro, Shadow Hand Quadrupeds: Anybotics Anymal-B, Anymal-C, Anymal-D, Unitree A1, Unitree Go1, Unitree Go2, Boston Dynamics Spot Humanoids: Unitree H1, Unitree G1 Quadcopter: Crazyflie Paper to read : https://arxiv.org/abs/2301.04195