Sim-to-Real gap in RL: Use Case with TIAGo and Isaac Sim/Gym

2024/03/11 by Albardaner, Jaume, Miguel, Alberto San, García, Néstor +1 · 2 citations
#FOS: Computer and information sciences #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2403.07091

Abstract

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. Presented results demonstrate successful sim-to-real transfer, showcasing similar movements executed by an RL-trained model in both simulated and real setups.

Cited by

• Towards Scalable Spatial Intelligence via 2D-to-3D Data Lifting
• Probabilistic Human Intent Prediction for Mobile Manipulation: An Evaluation with Human-Inspired Constraints

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