Sim-to-Real gap in RL: Use Case with TIAGo and Isaac Sim/Gym
This paper explores policy-learning approaches in the context of sim-to-real transfer for robotic manipulation using a TIAGo mobile manipulator, focusing on two state-of-art simulators, Isaac Gym and Isaac Sim, both developed by Nvidia. Control architectures are discussed, with a particular emphasis on achieving collision-less movement in both simulation and the real environment.
Evidence notes
- Experiments use TIAGo as the physical evaluation platform.
- The reported results provide an independent evaluation on TIAGo hardware.
Company context
Develops service and humanoid robots for research, industrial, and public-facing environments, with a focus on modular platforms and long-term deployment in real-world settings.