> 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

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/isaaclab/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/isaaclab/_mcp/server.

# Additional Resources

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](https://developer.nvidia.com/blog/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](https://github.com/isaac-sim/IsaacLabEureka) provides the reward-generation pipeline for direct Isaac Lab environments.

* [Eureka! NVIDIA Research Breakthrough Puts New Spin on Robot Learning](https://blogs.nvidia.com/blog/eureka-robotics-research/) 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](/isaaclab/3.0/concepts/physics-backends) and
[/source/concepts/renderers](/isaaclab/3.0/concepts/renderers).

* [Simulation Performance Guide](https://docs.omniverse.nvidia.com/kit/docs/omni_physics/latest/dev_guide/guides/physics-performance.html) is a best practice guide for obtaining the best simulation performance from OmniPhysics.

* [Deploying Policies in Isaac Sim](https://docs.isaacsim.omniverse.nvidia.com/latest/isaac_lab_tutorials/tutorial_policy_deployment.html) 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](https://developer.nvidia.com/blog/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](https://developer.nvidia.com/blog/fast-track-robot-learning-in-simulation-using-nvidia-isaac-lab/) introduces Isaac Lab's robot-learning workflows.