Reinforcement Learning
A robot learning technique to develop autonomous, adaptable, and efficient robots.
Nissan
Image Credit: Agility, Apptronik, Fourier, Unitree
Workloads
Robotics
Industries
All Industries
Business Goal
Innovation
Products
NVIDIA AI Enterprise
NVIDIA Isaac GR00T
NVIDIA Isaac Lab
NVIDIA Isaac Sim
NVIDIA Omniverse
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Overview
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Technical Implementation
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Partner Ecosystem
Empower Physical Robots With Complex Skills Using Reinforcement Learning
As robots undertake increasingly complex tasks, traditional programming is falling short. Reinforcement learning (RL) closes this gap by letting robots train in simulation through trial and error to enhance skills in control, path planning, and manipulation. This reward-based learning fosters continuous adaptation, allowing robots to develop sophisticated motor skills for real-world automation tasks like grasping, locomotion, and complex manipulation.
GPU-Accelerated RL Training for Robotics
Traditional CPU-based training for robot RL often requires thousands of cores for complex tasks, which drives up costs for robot applications. NVIDIA-accelerated computing addresses this challenge with parallel processing capabilities that significantly accelerate the processing of sensory data in perception-enabled reinforcement learning environments. This boosts a robot’s capabilities to learn, adapt, and perform complex tasks in dynamic situations.
NVIDIA-accelerated computing platforms—including robot training frameworks like NVIDIA 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.
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Isaac Lab for Reinforcement Learning
Isaac Lab is a modular framework built on NVIDIA Isaac Sim™ that simplifies robot training workflows like 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) defines 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 offers four RL libraries for training the models with GPUs—StableBaselines3, RSL-RL, RL-Games, and SKRL.
- Scale: To scale training across multi-GPU and multi-node systems, developers can use OSMO for orchestrating multi-node training tasks on distributed infrastructure.
NVIDIA Isaac GR00T gives developers a new way to 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.
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Partner Ecosystem
See how our ecosystem is building their own robotics applications and services based on reinforcement learning and NVIDIA technologies.
