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arXiv · 2403.07091

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

Jaume Albardaner·Alberto San Miguel·Néstor García·Magí Dalmau-Moreno
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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.

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Jaume Albardaner, Alberto San Miguel, Néstor García, Magí Dalmau-Moreno. 2024-03-11. Sim-to-Real gap in RL: Use Case with TIAGo and Isaac Sim/Gym. https://arxiv.org/abs/2403.07091

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