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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., Quillen, D.: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int. J. Robot. Res. 37(4–5), 421–436 (2018). https://doi.org/10.1177/0278364917710318
Gu, S., et al.: Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2017)
Kalashnikov, D., et al.: Scalable deep reinforcement learning for vision-based robotic manipulation. In: Conference on Robot Learning. PMLR (2018)
Towers, M., et al.: Gymnasium (2023). https://zenodo.org/record/8127025
Raffin, A., et al.: Stable-baselines3: reliable reinforcement learning implementations. J. Mach. Learn. Res. 22(1), 12348–12355 (2021)
Todorov, E., Erez, T., Tassa, Y.: Mujoco: a physics engine for model-based control. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2012)
Makoviychuk, V., et al.: Isaac gym: high performance GPU-based physics simulation for robot learning. arXiv preprint arXiv:2108.10470 (2021)
NVIDIA. Isaac Sim Robotics Simulator. https://developer.nvidia.com/isaac-sim. Accessed 16 Apr 2024
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Albardaner, J., Miguel, A.S., García, N., Dalmau, M. (2024). Sim-to-Real Gap in RL: Use Case with TIAGo and Isaac Sim/Gym. In: Secchi, C., Marconi, L. (eds) European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-76424-0_61
Download citation
DOI: https://doi.org/10.1007/978-3-031-76424-0_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-76423-3
Online ISBN: 978-3-031-76424-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)