# High-Level Multi-Robot Trajectory Planning And Spurious Behavior Detection

arXiv: https://arxiv.org/abs/2510.17261 · HTML: https://mlsanity.com/paper/2510.17261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning](https://mlsanity.com/paper/2207.03530/) (2022)
- [MT* : Multi-Robot Path Planning for Temporal Logic Specifications](https://mlsanity.com/paper/2103.02821/) (2021)
- [Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception](https://mlsanity.com/paper/2008.12610/) (2020)
- [Multi-robot Motion Planning based on Nets-within-Nets Modeling and Simulation](https://mlsanity.com/paper/2304.08772/) (2023)

## Abstract (arXiv)

The reliable execution of high-level missions in multi-robot systems with heterogeneous agents, requires robust methods for detecting spurious behaviors. In this paper, we address the challenge of identifying spurious executions of plans specified as a Linear Temporal Logic (LTL) formula, as incorrect task sequences, violations of spatial constraints, timing inconsistencies, or deviations from intended mission semantics. To tackle this, we introduce a structured data generation framework based on the Nets-within-Nets (NWN) paradigm, which coordinates robot actions with LTL-derived global mission specifications. We further propose a Transformer-based anomaly detection pipeline that classifies robot trajectories as normal or anomalous. Experimental evaluations show that our method achieves high accuracy (91.3%) in identifying execution inefficiencies, and demonstrates robust detection capabilities for core mission violations (88.3%) and constraint-based adaptive anomalies (66.8%). An ablation experiment of the embedding and architecture was carried out, obtaining successful results where our novel proposition performs better than simpler representations.