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Failure is Not an Option: Policy Learning for Adaptive Recovery in Space Operations

McGuire, S; Furlong, PM; Heckman, C; Julier, S; Szafir, D; Ahmed, N; (2018) Failure is Not an Option: Policy Learning for Adaptive Recovery in Space Operations. IEEE Robotics and Automation Letters , 3 (3) pp. 1639-1646. 10.1109/LRA.2018.2801468. Green open access

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Abstract

This letter considers the problem of how robots in long-term space operations can learn to choose appropriate sources of assistance to recover from failures. Current assistant selection methods for failure handling are based on manually specified static lookup tables or policies, which are not responsive to dynamic environments or uncertainty in human performance. We describe a novel and highly flexible learning-based assistant selection framework that uses contextual multiarm bandit algorithms. The contextual bandits exploit information from observed environment and assistant performance variables to efficiently learn selection policies under a wide set of uncertain operating conditions and unknown/dynamically constrained assistant capabilities. Proof of concept simulations of long-term human-robot interactions for space exploration are used to compare the performance of the contextual bandit against other state-of-the-art assistant selection approaches. The contextual bandit outperforms conventional static policies and noncontextual learning approaches, and also demonstrates favorable robustness and scaling properties.

Type: Article
Title: Failure is Not an Option: Policy Learning for Adaptive Recovery in Space Operations
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/LRA.2018.2801468
Publisher version: https://doi.org/10.1109/LRA.2018.2801468
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Robots, Task analysis, Resource management, Monitoring, Space missions, Heuristic algorithms, Earth
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10062810
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