Reinforcement Learning

Robot learning technique to develop adaptable and efficient robotic applications.

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Image Credit: Agility, Apptronik, Fourier Intelligence, Unitree

Workloads

Robotics

Industries

All Industries

Business Goal

Innovation

Products

NVIDIA Omniverse
NVIDIA Omniverse Enterprise
NVIDIA AI Enterprise

Empower Physical Robots With Complex Skills Using Reinforcement Learning

As robots take on more complex tasks, traditional programming methods become insufficient. Reinforcement learning (RL) is a machine learning technique designed to address this challenge by programming robot behavior. With RL in simulation, robots can train in any virtual environment through trial and error, enhancing their skills in control, path planning, manipulation, and more. 

The RL model gets rewarded for desired actions, so it’s constantly adapting and improving. This helps robots develop sophisticated gross and fine motor skills needed for real-world automation tasks such as grasping novel objects, quadrupedal walking, and learning complex manipulation skills.

By continuously refining control policies based on rewards and analyzing their actions, RL can also help robots adjust to new situations and unforeseen challenges, making them more adaptable for real-world tasks.

GPU-Accelerated RL Training for Robotics

Traditional CPU-based training for robot RL can be expensive, often requiring thousands of cores for complex tasks that drive up costs for robot applications. NVIDIA GPUs address this challenge with their parallel processing capabilities, significantly accelerating the processing of sensory data in perception-enabled reinforcement learning environments. This significantly enhances robots' capabilities to learn, adapt, and perform complex tasks in dynamic environments.

NVIDIA's compute platforms—including tools like Isaac Lab—take advantage of GPU power for both physics simulations and reward calculations within the RL pipeline. This eliminates bottlenecks and streamlines the process, facilitating a smoother transition from simulation to real-world deployment.

Isaac Lab for Reinforcement Learning

NVIDIA Isaac™ Lab is modular framework built on NVIDIA Isaac Sim™ that simplifies robot training workflows such as reinforcement and imitation learning. Developers are able to leverage 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 reward function. The environment (e.g. Manager-Based or Direct-Workflow) provides the agent’s current state or observations, 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.

Project GR00T offers developers a new way to specifically develop humanoid robots. GR00T is a general-purpose foundation model that 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.

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Reinforcement learning for robotics is widely adopted by today’s researchers and developers. Learn more about NVIDIA Isaac Lab for robot learning today.

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