Physics Simulation for Robotics

Physics simulation for robotics uses computational models of motion, contact, friction, actuation, sensors, and materials to represent how robots and their environments interact. GPU-accelerated simulation can run many environments in parallel, giving teams a scalable way to generate training experience, test edge cases, and evaluate robot behavior before moving to hardware.

Teams can use simulation to:

• Start development before hardware is ready. Build and test robot behaviors in virtual environments while physical systems are still being assembled.
• Run more experiments in less time. Test many tasks, operating conditions, and design variations without executing each experiment on a physical robot.
• Reduce development cost and risk. Identify failures before they damage equipment, interrupt operations, or create safety risks.
• Improve confidence before deployment. Evaluate robot behavior across repeatable scenarios, disturbances, and edge cases.
• Create a path from experimentation to production. Use simulation to train, refine, and validate robot behaviors, then confirm results through real-world testing.

Learn more about robot learning.

Physics simulation supports robotics workflows ranging from large-scale policy training to contact-rich assembly, deformable-object manipulation, and policy evaluation before deployment. Explore the following examples to see how simulation applies to each workflow.

Robot Learning and Policy Training
Physics simulation creates the interactive experience a robot policy needs to learn. Developers can define observations, actions, rewards, resets, and success criteria, run many environments in parallel, and evaluate whether the learned behavior is stable before moving to hardware.

GPU-Accelerated Reinforcement Learning
Robot-learning workloads often prioritize throughput across many parallel environments rather than the lowest latency for a single simulation step. GPU-accelerated simulation can keep physics and policy computation on the GPU, reduce data-transfer overhead, and increase the amount of experience produced during training. The benefit depends on the workload, batch size, contact complexity, and hardware, so developers should benchmark their own setup.

Contact-Rich Manipulation and Assembly
Insertion, fastening, grasping, in-hand manipulation, and tactile tasks depend on accurate contact geometry, friction, compliance, actuator behavior, and tight tolerances. Simulation enables teams to tune these parameters, investigate failure modes, and train policies across variations that would be tedious or risky to reproduce physically.

Deformable Objects and Multiphysics Tasks
Many robotics tasks combine rigid mechanisms with cables, cloth, soft bodies, granular materials, or other deformable objects. These workflows may require a specialized solver or coupled physics approach rather than a rigid-body-only model. A modular simulation stack lets developers select or extend the physics needed for the task.

Policy Evaluation and Transfer
Simulation provides a controlled test bed for comparing policies, testing disturbances and edge cases, and checking whether behavior remains acceptable after changing a solver or simulation backend. Sim-to-sim and sim-to-real evaluation can expose sensitivity to model assumptions and guide calibration, domain randomization, and further data collection.

The robotics simulation stack separates task definition, physics computation, GPU acceleration, and policy training so developers can select the components their workflow requires.

• MuJoCo Warp (MJ Warp) provides widely used CPU-based rigid-body dynamics, contact simulation, modeling, and control capabilities for robotics and reinforcement learning.
• MJWarp implements MuJoCo physics in NVIDIA Warp for high-throughput parallel simulation on NVIDIA GPUs. It is most relevant when a workload benefits from many environments running concurrently.
• NVIDIA Warp is the Python framework used to build GPU-accelerated simulation and spatial-computing kernels and connect them with machine-learning frameworks.
• Newton is an open-source, extensible physics engine built on NVIDIA Warp and OpenUSD. It separates the physical model, state, controls, contacts, and solver so developers can use the numerical approach appropriate for the problem. MJWarp is a key rigid-body solver in Newton.
• The NVIDIA Isaac Sim™ open framework serves as an authoring environment for assets that can be simulated with Newton. Developers can rig robots with multiple physics backends and simulate downstream in either Isaac Sim or Isaac Lab. Isaac Sim also enables software-in-the-loop (SIL) testing of robot software, including policies trained in Isaac Lab, with Newton as the physics backend
• NVIDIA Isaac Lab is an open-source, GPU-accelerated simulation framework for robot learning. It supports environment configuration, policy training, evaluation, and transfer workflows and can use Newton as a physics backend.

NVIDIA works with Google DeepMind, Disney Research, and the broader Linux Foundation community to advance Newton’s GPU-accelerated physics, solver ecosystem, simulation assets, and robot-learning workflows.

Explore the Newton open ecosystem or contact NVIDIA to identify the resources and collaborators best suited to your robotics simulation needs.