Additional Resources

This page is for version Release 3.0 (default). For other versions, use one of these documentation indexes: - Release 3.0 (default): https://docs.nvidia.com/isaaclab/3.0/llms.txt - Release 2.3: https://docs.nvidia.com/isaaclab/2.3/llms.txt - Release 2.2: https://docs.nvidia.com/isaaclab/2.2/llms.txt - Release 2.1: https://docs.nvidia.com/isaaclab/2.1/llms.txt

External tutorials, research projects, and tools for robot learning and deployment.

Sim-to-Real Resources

Examples of training policies in simulation and deploying them on robots:

• Closing the Sim-to-Real Gap: Training Spot Quadruped Locomotion with NVIDIA Isaac Lab is a detailed guide for training a quadruped locomotion policy for the Spot Quadruped from Boston Dynamics, and deploying it to the real robot.

LLM Generated Reward Functions

Eureka uses an LLM to generate and tune reinforcement-learning reward functions.

• Isaac Lab Eureka provides the reward-generation pipeline for direct Isaac Lab environments.

• Eureka! NVIDIA Research Breakthrough Puts New Spin on Robot Learning is a blog post that covers the broad idea of this reward generation process.

Simulation Features

The following resources cover Isaac Sim workflows. For Isaac Lab’s physics and rendering options, see /source/concepts/physics_backends and /source/concepts/renderers.

• Simulation Performance Guide is a best practice guide for obtaining the best simulation performance from OmniPhysics.

• Deploying Policies in Isaac Sim is an Isaac Sim tutorial on how to use trained policies within the simulation.

• Supercharge Robotics Workflows with AI and Simulation Using NVIDIA Isaac Sim 4.0 and NVIDIA Isaac Lab describes the features introduced in Isaac Sim 4.0.

• Fast-Track Robot Learning in Simulation Using NVIDIA Isaac Lab introduces Isaac Lab’s robot-learning workflows.