The XPro project tackles an important problem in engineering and robotics, namely the problem of probabilistic explainability of deep-models for robot-critical applications such as autonomous robot-perception, self-driving robot-vehicles, and decision making. In this project we will focus on explainability (which is not the same as interpretability) by developing post-hoc techniques applied to existing pre-trained deep models applied to robot perception. In terms of case studies, XPro will explore two application domains, robotic perception and autonomous robotic-vehicles. Collectively, the XPro team will work in collaboration with young and senior researchers, as well as PhD students, for developing new probabilistic-based post-hoc calibration towards explainable models, new uncertainty-quantification evaluation metrics, real-world application domains (ie, relevant case-studies), dissemination and short-term missions will be carried-out as well.

Objectives

• Developing new explainability frameworks for existing pre-trained deep architectures applied to critical/safety classification in mobile robotic problems.

• Evaluating the proposed frameworks, and their components, by means of SOTA existing and novel quantification metrics that take account for partial information and misclassification.

• Exploration and validation of the developed frameworks in relevant application domains, namely perception systems for robots and self-driving (object detection).