Maximilian Diehl

Maximilian Diehl is a Ph.D. student at the Department of Electrical Engineering at the Chalmers University of Technology in Sweden since 2020. Previously, he obtained his Bachelor's and Master's Degrees in Electrical Engineering from the Technical University of Munich (TUM) in 2018 and 2019, respectively. In addition, he received a JASSO scholarship to pursue his Master's Thesis project at the Nara Institute of Science and Technology in Japan at the Interactive Media Design Laboratory in collaboration with the Institute for Cognitive Systems (ICS) at the TUM in 2019. Maximilian's research interests include Explanation, prediction, and prevention of robot task execution failures; Causality in Robotics; Robot Task planning; Augmented and Virtual Reality

• Electrical circuits and fields (Bachelor program), Role: Teaching Assistant, Chalmers, 2023 - Now

• Decision making for autonomous system (Master program), Role: Teaching Assistant, Chalmers, 2020 - Now

• Artificial intelligence and autonomous systems (Bachelor program), Role: Teaching Assistant, Chalmers, 2020 - Now

• Model-based development of cyber-physical systems (Master program), Role: Teaching Assistant, Chalmers, 2020

• Introduction/Advanced Humanoid RoboCup (Master program), Role: Teaching Assistant, TUM, 2018

• Online Practical Course Digital Speech and Image Processing (Bachelor program), Role: Tutor,  TUM, 2016-2017

Workshop Organization

• Co-organizer of the HAI Workshop ”The Importance of Human Factors for Trusted Human-Robot Collaborations,” Accepted to be a full-day workshop proposed for December 4th, 2023, as part of the 11th International Conference on Human-Agent Interaction, 2023, Gothenburg, Sweden.

• Co-organizer of the ICRA Workshop ”Workshop on Robot Execution Failures and Failure Management Strategies,”  Accepted to be a full-day workshop proposed for June 2nd, 2023, as part of the IEEE International Conference on Robotics and Automation (ICRA), London, England.

Publications

• Journals: 

  • Maximilian Diehl and Karinne Ramirez-Amaro,  ”Generating and Transferring Priors for Causal Bayesian Network Parameter Estimation in Robotic Tasks, ” IEEE Robotics and Automation Letters, 2024.

  • Maximilian Diehl and Karinne Ramirez-Amaro, ”A causal-based approach to explain, predict and prevent failures in robotic tasks,” Robotics and Autonomous Systems (RAS), Elsevier, 2023.

  • Maximilian Diehl and Karinne Ramirez-Amaro, ”Why did I fail? A Causal-based Method to Find Explanations for Robot Failures”, IEEE Robotics and Automation Letters, 2022.

• Conferences: 

  • Maximilian Diehl, Chris Paxton, and Karinne Ramirez-Amaro, ”Automated Generation of Robotic Planning Domains from Observations,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, Online. 2021.

  • Maximilian Diehl, Alexander Plopski, Hirokazu Kato, Karinne Ramirez-Amaro, ”Augmented Reality interface to verify Robot Learning,” IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Naples, Italy, Online. 2020.