NVIDIA Isaac™ Lab is an open-source, unified framework for robot learning designed to help train robot policies. Developed on NVIDIA Isaac Sim™, it provides high-fidelity physics simulation using NVIDIA PhysX® and physically based NVIDIA RTX™ rendering. Isaac Lab bridges the gap between high-fidelity simulation and perception-based robot training, helping developers and researchers build more robots, more efficiently.

Features

• Modular Architecture: Ideal for building robot policies for various embodiments, including humanoid robots, manipulators, and autonomous mobile robots (AMRs).
• Customizable Workflows: Customize and extend capabilities with various physics engines, including NVIDIA PhysX, Warp, and MuJoCo.
• Flexible Robot Learning: Supports reinforcement learning, imitation learning, and demonstration learning.
• High-Fidelity Physics Simulation: GPU-accelerated PhysX version provides accurate simulations, including support for deformables.
• Unified Representation: Easy customization and addition of new environments, robots, and sensors with OpenUSD.
• Multi-GPU and Multi-Node Training: Scale up training across multiple GPUs and nodes, deploy locally or on the cloud.

Benefits

• Efficient Training: Reduces the need for extensive hardware expenses and time-intensive policy training iterations.
• Enhanced Realism: High-fidelity simulations ensure realistic modeling of robot interactions with the environment.
• Scalability: Supports large-scale training and deployment, making it suitable for complex reinforcement learning environments.
• Versatility: Applicable to a wide range of robotic applications, from humanoid robots to autonomous mobile robots.