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.
@article{arxiv.2403.07091,
title = {Sim-to-Real gap in RL: Use Case with TIAGo and Isaac Sim/Gym},
author = {Jaume Albardaner and Alberto San Miguel and Néstor García and Magí Dalmau-Moreno},
journal= {arXiv preprint arXiv:2403.07091},
year = {2024}
}
Comments
Accepted in ERF24 workshop "Towards Efficient and Portable Robot Learning for Real-World Settings". To be published in Springer Proceedings in Advanced Robotics