Enhancing Space Manipulator Fault Tolerance for In-Orbit Servicing through Meta-Reinforcement Learning
Matteo D’AMBROSIO, Michèle LAVAGNA
Abstract. This study develops a data-driven motion planning framework for a 7-DoF robotic manipulator to enhance fault tolerance against joint failures during the pre-capture phase of In-Orbit Servicing missions. The system is trained using meta-Reinforcement Learning (meta-RL), exposing the agent to a wide range of randomized scenarios, including stochastic joint-locking. This process achieves two main goals: it equips the agent with strong generalization capabilities for conditions beyond its training domain, and it bypasses traditional inverse kinematics by learning a direct mapping from sensor data to joint-space commands. The meta-learning framework is shown to significantly improve the system’s ability to autonomously manage unforeseen failure events. In non-critical scenarios, the agent successfully learns to repurpose redundant joints to complete its task. However, the system cannot recover from failures that cause an unrecoverable reduction of the manipulator’s workspace. The results demonstrate that this approach leads to more predictable autonomous behavior during failures and, most importantly, shows how some unexpected anomalies may be overcome by simply reframing them as an operational constraint, before critical operations begin.
Keywords
Orbital Robotics, Failure Management, Deep Reinforcement Learning
Published online 7/20/2026, 6 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Matteo D’AMBROSIO, Michèle LAVAGNA, Enhancing Space Manipulator Fault Tolerance for In-Orbit Servicing through Meta-Reinforcement Learning, Materials Research Proceedings, Vol. 69, pp 942-947, 2026
DOI: https://doi.org/10.21741/9781644904251-166
The article was published as article 166 of the book CEAS – AIDAA Conference 2025
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] A. Flores-Abad, O. Ma, K. Pham, S. Ulrich, A review of space robotics technologies for on-orbit servicing, Prog. Aerosp. Sci. 68 (2014) 1-26. https://doi.org/10.1016/j.paerosci.2014.03.002
[2] B.M. Moghaddam, R. Chhabra, On the guidance, navigation and control of in-orbit space robotic missions: A survey and prospective vision, (2021). https://doi.org/10.1016/j.actaastro.2021.03.029
[3] A. Al Ali, Z.H. Zhu, Reinforcement learning for path planning of free-floating space robotic manipulator with collision avoidance and observation noise, Front. Control Eng. 5 (2024). https://doi.org/10.3389/fcteg.2024.1394668
[4] S. Wang, X. Zheng, Y. Cao, T. Zhang, A multi-target trajectory planning of a 6-DoF free-floating space robot via reinforcement learning, in: Proc. IEEE, (2021) 3724-3730. https://doi.org/10.1109/IROS51168.2021.9636681
[5] J. Blaise, M.C.F. Bazzocchi, Space manipulator collision avoidance using a deep reinforcement learning control, Aerospace 10 (2023). https://doi.org/10.3390/aerospace10090778
[6] M. D’Ambrosio, L. Capra, A. Brandonisio, S. Silvestrini, M. Lavagna, Redundant space manipulator autonomous guidance for in-orbit servicing via deep reinforcement learning, Aerospace 11 (2024) 341. https://doi.org/10.3390/aerospace11050341
[7] B. Gaudet, R. Linares, R. Furfaro, Adaptive guidance and integrated navigation with reinforcement meta-learning, Acta Astronaut. 169 (2020) 180-190. https://doi.org/10.1016/j.actaastro.2020.01.007
[8] B. Gaudet, R. Linares, R. Furfaro, Terminal adaptive guidance via reinforcement meta-learning: Applications to autonomous asteroid close-proximity operations, Acta Astronaut. 171 (2020) 1-13. https://doi.org/10.1016/j.actaastro.2020.02.036
[9] M. D’Ambrosio, S. Silvestrini, M. Lavagna, Conditioned sequence models for warm-starting sequential convex trajectory optimization in space robots, Aerospace 13 (2026) 137. https://doi.org/10.3390/aerospace13020137. https://doi.org/10.3390/aerospace13020137
[10] J. Virgili-Llop, J.V. Drew II, M. Romano, SPART spacecraft robotics toolkit: an open-source simulator for spacecraft robotic arm dynamic modeling and control, (2016).
[11] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, Proximal policy optimization algorithms, arXiv:1707.06347 (2017).
[12] Y. Li, D. Li, W. Zhu, J. Sun, X. Zhang, S. Li, Constrained motion planning of 7-DoF space manipulator via deep reinforcement learning combined with artificial potential field, Aerospace 9 (2022). https://doi.org/10.3390/aerospace9030163
