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

Autor:	Albardaner, Jaume, Miguel, Alberto San, García, Néstor, Dalmau-Moreno, Magí
Rok vydání:	2024
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted in ERF24 workshop "Towards Efficient and Portable Robot Learning for Real-World Settings". To be published in Springer Proceedings in Advanced Robotics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.07091 Zobrazit plný text záznamu View this record from Arxiv