Reinforcement Learning

Isaac Lab is a modular framework built on NVIDIA Isaac Sim™ that simplifies robot training workflows such as reinforcement and imitation learning. Developers can take advantage of the latest Omniverse™ capabilities for training complex policies with perception enabled.

• Assemble the Scene: The first step is to build a scene in Isaac Sim or Isaac Lab and import robot assets from URDF or MJCF. Apply physics schemas for simulation and integrate sensors for perception-based policy training.
• Define RL Tasks: Once the scene and robot have been configured, the next step is to define the task to be completed and the reward function. The environment (e.g. Manager-Based or Direct Workflow) provides the agent’s current state and observes and executes the actions it provides. The environment then responds to the agents by providing the next states.
• Train: The last step is to define the hyperparameters for training and the policy architecture. Isaac Lab provides four RL libraries for training the models with GPUs—StableBaselines3, RSL-RL, RL-Games, and SKRL.
• Scale: To scale the training across multi-GPU and multi-node systems, developers can use OSMO to orchestrate multi-node training tasks on distributed infrastructure.

NVIDIA Isaac GR00T offers developers a new way to specifically develop humanoid robots. This research initiative and development platform for general-purpose robot foundation models and data pipelines can help understand language, emulate human movements, and rapidly acquire skills through multi-modal learning.

To learn more and access GR00T, apply to the NVIDIA Humanoid Developer Program.

See how our ecosystem is building their own robotics applications and services based on reinforcement learning and NVIDIA technologies.