Advancing Robotics Development with Neural Dynamics in Newton
Advancing Robotics Development with Neural Dynamics in Newton | NVIDIA Technical Blog
“Modern robotics requires more than what classical analytic dynamics provides because of simplified contacts, omitted kinematic loops, and non-differentiable models. Neural Robot Dynamics (NeRD) tackles these hurdles by:
- Using expressive, differentiable models that predict stable states over long horizons.
- Capturing complex contact-rich physics.
- Generalizing across tasks, environments, and controllers, narrowing the sim-to-real gap.
- Fine-tuning on real data.
Unlike task-specific neural simulators, NeRD serves as a drop-in backend within physics engines like Newton, enabling teams to reuse existing policy-learning environments by simply switching the physics solver. This hybrid of analytical modules with robot-centric neural modeling paves the way for robots whose dynamics continually improve through both simulation and real-world experience.
In this post, we explore how NeRD overcomes longstanding simulation challenges, providing the foundation for modern robotics in physics engines like Newton…”
Source: developer.nvidia.com/blog/advancing-robotics-development-with-neural-dynamics-in-newton/