# Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems

arXiv: https://arxiv.org/abs/2109.12343 · HTML: https://mlsanity.com/paper/2109.12343/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 101 of its 288 references that are in the map.
- First posted on arXiv 25 Sep 2021.

## Builds on (most cited first, 30 of 101)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Robustness May Be at Odds with Accuracy](https://mlsanity.com/paper/1805.12152/) (2018)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [DSAC - Differentiable RANSAC for Camera Localization](https://mlsanity.com/paper/1611.05705/) (2016)
- [A General and Adaptive Robust Loss Function](https://mlsanity.com/paper/1701.03077/) (2017)
- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020)
- [Generative Adversarial Perturbations](https://mlsanity.com/paper/1712.02328/) (2017)
- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans](https://mlsanity.com/paper/2002.06289/) (2020)
- [Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection](https://mlsanity.com/paper/1909.08605/) (2019)
- [Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1906.04737/) (2019)
- [Graph Neural Networks for Decentralized Multi-Robot Path Planning](https://mlsanity.com/paper/1912.06095/) (2019)
- [Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition](https://mlsanity.com/paper/1907.13369/) (2019)
- [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017)
- [Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks](https://mlsanity.com/paper/1903.10527/) (2019)
- [Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems](https://mlsanity.com/paper/2106.14386/) (2021)
- [Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks](https://mlsanity.com/paper/2108.01176/) (2021)
- [Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning](https://mlsanity.com/paper/2011.13219/) (2020)
- [Scalable Co-Optimization of Morphology and Control in Embodied Machines](https://mlsanity.com/paper/1706.06133/) (2017)
- [Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning](https://mlsanity.com/paper/2005.05420/) (2020)
- [Outside the Box: Abstraction-Based Monitoring of Neural Networks](https://mlsanity.com/paper/1911.09032/) (2019)
- [DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams](https://mlsanity.com/paper/1909.12198/) (2019)
- [Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks](https://mlsanity.com/paper/2012.05137/) (2020)

## Built on by (most cited first, 12 of 12)

- [System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning](https://mlsanity.com/paper/2305.02128/) (2023)
- [SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2301.04299/) (2023)
- [Compositional Coordination for Multi-Robot Teams with Large Language Models](https://mlsanity.com/paper/2507.16068/) (2025)
- [Resilience Evaluation of Entropy Regularized Logistic Networks with Probabilistic Cost](https://mlsanity.com/paper/2212.02060/) (2022)
- [Optimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets](https://mlsanity.com/paper/2209.06316/) (2022)
- [Fault-Tolerant Offline Multi-Agent Path Planning](https://mlsanity.com/paper/2211.13908/) (2022)
- [Prediction of SLAM ATE Using an Ensemble Learning Regression Model and 1-D Global Pooling of Data Characterization](https://mlsanity.com/paper/2303.00616/) (2023)
- [A Model for Optimal Resilient Planning Subject to Fallible Actuators](https://mlsanity.com/paper/2405.11402/) (2024)
- [Worst-Case Distance-Aware Error Bounds for Neural Networks](https://mlsanity.com/paper/2510.22021/) (2025)
- [Active Matter as a framework for living systems-inspired Robophysics](https://mlsanity.com/paper/2511.14624/) (2025)
- [Logic-Driven Semantic Communication for Resilient Multi-Agent Systems](https://mlsanity.com/paper/2601.06733/) (2026)
- [Resilience Matters for Embodied Agents System: New Metrics, Systematic Evaluation, and Optimization](https://mlsanity.com/paper/2608.23839/) (2026)

## Abstract (arXiv)

Robustness is key to engineering, automation, and science as a whole. However, the property of robustness is often underpinned by costly requirements such as over-provisioning, known uncertainty and predictive models, and known adversaries. These conditions are idealistic, and often not satisfiable. Resilience on the other hand is the capability to endure unexpected disruptions, to recover swiftly from negative events, and bounce back to normality. In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by leveraging system-wide complementarity, diversity, and redundancy - often involving a reconfiguration of robotic capabilities to provide some key ability that was not present in the system a priori. As society increasingly depends on connected automated systems to provide key infrastructure services (e.g., logistics, transport, and precision agriculture), providing the means to achieving resilient multi-robot systems is paramount. By enumerating the consequences of a system that is not resilient (fragile), we argue that resilience must become a central engineering design consideration. Towards this goal, the community needs to gain clarity on how it is defined, measured, and maintained. We address these questions across foundational robotics domains, spanning perception, control, planning, and learning. One of our key contributions is a formal taxonomy of approaches, which also helps us discuss the defining factors and stressors for a resilient system. Finally, this survey article gives insight as to how resilience may be achieved. Importantly, we highlight open problems that remain to be tackled in order to reap the benefits of resilient robotic systems.