# Discovering Antagonists in Networks of Systems: Robot Deployment

arXiv: https://arxiv.org/abs/2502.20125 · HTML: https://mlsanity.com/paper/2502.20125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots](https://mlsanity.com/paper/2209.12285/) (2022)
- [Decentralized Vision-Based Byzantine Agent Detection in Multi-Robot Systems with IOTA Smart Contracts](https://mlsanity.com/paper/2210.03441/) (2022)
- [Distributed Detection and Mitigation of Biasing Attacks over Multi-Agent Networks](https://mlsanity.com/paper/2109.09329/) (2021)

## Abstract (arXiv)

A contextual anomaly detection method is proposed and applied to the physical motions of a robot swarm executing a coverage task. Using simulations of a swarm's normal behavior, a normalizing flow is trained to predict the likelihood of a robot motion within the current context of its environment. During application, the predicted likelihood of the observed motions is used by a detection criterion that categorizes a robot agent as normal or antagonistic. The proposed method is evaluated on five different strategies of antagonistic behavior. Importantly, only readily available simulated data of normal robot behavior is used for training such that the nature of the anomalies need not be known beforehand. The best detection criterion correctly categorizes at least 80% of each antagonistic type while maintaining a false positive rate of less than 5% for normal robot agents. Additionally, the method is validated in hardware experiments, yielding results similar to the simulated scenarios. Compared to the state-of-the-art approach, both the predictive performance of the normalizing flow and the robustness of the detection criterion are increased.