Interview with Amar Halilovic: Explainable AI for robotics - Robohub

Amar Halilovic, a PhD student at Ulm University in Germany, is conducting research on explainable AI (XAI) for robotics, focusing on how robots can generate explanations of their actions—particularly in navigation—that align with human preferences and expectations. His work involves developing frameworks for environmental explanations, especially in failure scenarios, using black-box and generative methods to produce textual and visual explanations. He also studies how to plan explanation attributes such as timing, representation, and duration, and is currently exploring dynamic selection of explanation strategies based on context and user preferences. Halilovic finds it particularly interesting how people interpret robot behavior differently depending on urgency or failure context, and how explanation expectations shift accordingly. Moving forward, he plans to extend his framework to enable real-time adaptation, allowing robots to learn from user feedback and adjust explanations on the fly. He also aims to conduct more user studies to validate the effectiveness of these explanations in real-world human-robot interaction settings. His motivation for studying explainable robot navigation stems from a broader interest in human-machine interaction and the importance of understandable AI for trust and usability. Before his PhD, Amar studied Electrical Engineering and Computer Science in Bosnia and Herzegovina and Sweden. Outside of research, he enjoys traveling and photography and values building a supportive network of mentors and peers for success in doctoral studies. His interdisciplinary approach combines symbolic planning and machine learning to create context-sensitive, explainable robot systems that adapt to diverse human needs.