mlsanityData to 7 Oct 2026

Resilient robot teams: a review integrating decentralised control, change-detection, and learning

arXiv 2204.10063 ↗First posted 21 Apr 2022cs.RO cs.AIcited by 1

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Abstract (arXiv)

Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams. Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action-communication loops, multi-agent reinforcement learning, embodied evolution, offline evolution with online adaptation, explicit task allocation, and stigmergy in swarm robotics. Summary: Remaining challenges for resilient robot teams are integrating change-detection and trial-and-error learning methods, obtaining reliable performance evaluations under constrained evaluation time, improving the safety of resilient robot teams, theoretical results demonstrating rapid adaptation to given environmental perturbations, and designing realistic and compelling case studies.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 101 references that are in the map. First posted on arXiv 21 Apr 2022.

Builds on

30 of 101 references.

References · 30

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18 more
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  1. 2025Enhancing Social Robots through Resilient AIcited by 0