A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents

TL;DR

This paper introduces a multi-perspective anomaly detection method for self-aware embodied agents, combining external and internal visual data to model and predict agent trajectories using Gaussian processes and GANs.

Contribution

It presents a novel multi-perspective approach integrating Gaussian processes and GANs for anomaly detection in self-aware embodied agents.

Findings

Multi-perspective data improves trajectory prediction accuracy.

Gaussian process models effectively characterize agent motion.

GANs can estimate internal and external agent parameters.

Abstract

This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations. Both observation types are used to characterize agents' motion in a given environment. The proposed method generates locally uniform motion models by dividing a Gaussian process that approximates agents' displacements on the scene and provides a Shared Level (SL) self-awareness based on Environment Centered (EC) models. Such models are then used to train in a semi-unsupervised way a set of Generative Adversarial Networks (GANs) that produce an estimation of external and internal parameters of moving agents. Obtained results exemplify the feasibility of using multi-perspective data for predicting and analyzing trajectory information.