Technology Category
Computer Vision: Adversarial Attacks & RobustnessIntelligent Robots: State EstimationMachine Learning: Adversarial Learning & Robustness
Multi-Robot Coordination with Adversarial Perception
🤖 AI Summary
This work addresses the robust cooperative control of safety-critical multi-UAV systems under online adversarial perception attacks—such as false detections, mislocalizations, and measurement delays. We first systematically model such attacks as intermittent and spurious measurements. To counter them, we propose a resilient relative localization and state estimation framework that fuses visual-inertial odometry (VIO) with multi-task learning–based perception outputs. Innovatively, we formulate a quantifiable model of observability and stability degradation, enabling real-time resilience assessment on resource-constrained platforms. Our method integrates robust filtering, intermittent measurement handling, and observability-theoretic analysis. Experimental validation on a real multi-robot platform demonstrates significant improvements in relative localization accuracy and cooperative stability under adversarial conditions; moreover, performance degradation is quantitatively characterized by attack success rate.
Technology Category
Application Category
📝 Abstract
This paper investigates the resilience of perception-based multi-robot coordination with wireless communication to online adversarial perception. A systematic study of this problem is essential for many safety-critical robotic applications that rely on the measurements from learned perception modules. We consider a (small) team of quadrotor robots that rely only on an Inertial Measurement Unit (IMU) and the visual data measurements obtained from a learned multi-task perception module (e.g., object detection) for downstream tasks, including relative localization and coordination. We focus on a class of adversarial perception attacks that cause misclassification, mislocalization, and latency. We propose that the effects of adversarial misclassification and mislocalization can be modeled as sporadic (intermittent) and spurious measurement data for the downstream tasks. To address this, we present a framework for resilience analysis of multi-robot coordination with adversarial measurements. The framework integrates data from Visual-Inertial Odometry (VIO) and the learned perception model for robust relative localization and state estimation in the presence of adversarially sporadic and spurious measurements. The framework allows for quantifying the degradation in system observability and stability in relation to the success rate of adversarial perception. Finally, experimental results on a multi-robot platform demonstrate the real-world applicability of our methodology for resource-constrained robotic platforms.
Problem
Research questions and friction points this paper is trying to address.
Resilience of multi-robot coordination under adversarial perception attacks
Robust state estimation with sporadic adversarial measurements
Quantifying system degradation due to adversarial perception success rate
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adversarial perception resilience in multi-robot coordination
Visual-Inertial Odometry integrated with learned perception
Framework for robust state estimation under attacks
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