A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents
This work addresses anomaly detection for self-aware embodied agents, but it appears incremental as it builds on existing techniques like GANs and Gaussian processes.
The paper tackles multi-sensor anomaly detection for moving cognitive agents by using external and first-person visual observations to characterize motion, resulting in a method that demonstrates feasibility for predicting and analyzing trajectory information.
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.