XPro

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 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, to develop new probabilistic-based posthoc 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.

Case Studies: Robotic Perception, Autonomous Driving

Short facts:


Project-related publications:

  • [C3] "Explaining Demands for Ride-pooling Services: A Case Study in Berlin". Martin Aleksandrov. In Proceedings of the 35th Intelligent Vehicle Symposium (IEEE IV 2024), June 2-5, 2024, Jeju Island, Korea.
  • [C2] "Explaining Artificial Neural Networks". Martin Aleksandrov. In Proceedings of the International Conference on Business and Artificial Intelligence Technologies (ICBAIT 2024), April 25-26, 2024, Berlin, Germany.
  • [C1] TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition. Tiago Barros, Luis Garrote, Martin Damyanov Aleksandrov, Cristiano Premebida, Urbano J. Nunes. ITSC 2023, 24-28 September 2023, Bilbao, Spain.