Sim-to-Real gap in RL: Use Case with TIAGo and Isaac Sim/Gym
<|reference_start|>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. Presented results demonstrate successful sim-to-real transfer, showcasing similar movements executed by an RL-trained model in both simulated and real setups.<|reference_end|>
@article{albardaner2024sim-to-real,
    title={Sim-to-Real gap in RL: Use Case with TIAGo and Isaac Sim/Gym},
    author={Jaume Albardaner, Alberto San Miguel, N'estor Garc'ia, Mag'i
  Dalmau-Moreno},
    journal={arXiv preprint arXiv:2403.07091},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07091},
    primaryClass={cs.RO}
}