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

arXiv: https://arxiv.org/abs/2204.10063 · HTML: https://mlsanity.com/paper/2204.10063/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 101 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 30 of 30)

- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Efficiently Identifying Task Groupings for Multi-Task Learning](https://mlsanity.com/paper/2109.04617/) (2021)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [Learning Attentional Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/1805.07733/) (2018)
- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning](https://mlsanity.com/paper/1802.01557/) (2018)
- [Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games](https://mlsanity.com/paper/1703.10069/) (2017)
- [Safe Multi-Agent Reinforcement Learning via Shielding](https://mlsanity.com/paper/2101.11196/) (2021)
- [Distributed Constraint Optimization Problems and Applications: A Survey](https://mlsanity.com/paper/1602.06347/) (2016)
- [The Robotarium: A remotely accessible swarm robotics research testbed](https://mlsanity.com/paper/1609.04730/) (2016)
- [Continual Reinforcement Learning in 3D Non-stationary Environments](https://mlsanity.com/paper/1905.10112/) (2019)
- [Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks](https://mlsanity.com/paper/1602.02672/) (2016)
- [Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations](https://mlsanity.com/paper/2010.15896/) (2020)
- [QED: using Quality-Environment-Diversity to evolve resilient robot swarms](https://mlsanity.com/paper/2003.02341/) (2020)
- [Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks](https://mlsanity.com/paper/2106.13358/) (2021)
- [Adaptive Task Allocation for Heterogeneous Multi-Robot Teams with Evolving and Unknown Robot Capabilities](https://mlsanity.com/paper/2003.03344/) (2020)
- [Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation](https://mlsanity.com/paper/2012.11444/) (2020)
- [Secure and secret cooperation in robotic swarms](https://mlsanity.com/paper/1904.09266/) (2019)
- [Data-Driven Adaptive Task Allocation for Heterogeneous Multi-Robot Teams Using Robust Control Barrier Functions](https://mlsanity.com/paper/2011.01164/) (2020)
- [Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors](https://mlsanity.com/paper/1611.09419/) (2016)
- [Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm](https://mlsanity.com/paper/1804.07655/) (2018)

## Built on by (most cited first, 2 of 2)

- [Enhancing Social Robots through Resilient AI](https://mlsanity.com/paper/2510.21469/) (2025)
- [A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms](https://mlsanity.com/paper/2511.02192/) (2025)

## 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.