Isaac Sim-to-Real: Reinforcement Learning based Locomotion for Quadrupeds
Learning-based approaches to locomotion have risen in popularity in recent years, showing the capability for complex legged locomotion and whole-body control. Reinforcement learning (RL), the primary learning-based approach for locomotion, often utilizes a high-performance simulation tool, providing a controlled and efficient training and development environment. However, policies that perform well in simulation frequently encounter unexpected challenges when deployed on a physical system, known as the sim-to-real gap. This work presents a robust RL locomotion framework capable of whole-body control. The proposed RL framework utilizes Nvidia's new set of simulation tools, Isaac Sim, and i...
| Authors | Jordan Dowdy, Jean Chagas Vaz |
|---|---|
| Primary category | cs.RO |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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