Bayesian Fusion for Multimodal Human-Robot Interaction
Dr. Susanne Trick, Cognitive Science Lab at TU Darmstadt
When: Tuesday, 19th of May, 2026 at 11:00 AM
Where: NEXUS Lab, IMBIT, Georges-Koehler-Allee 201.
Abstract: To cope with perceptual uncertainty, humans integrate information from multiple sources, such as visual, auditory, and haptic input. A large body of evidence shows that this multimodal integration is often statistically optimal and can be explained by Bayes’ rule. In an inherently uncertain world, however, combining information from multiple sources is crucial not only for humans but also for robots, which are increasingly taking on tasks in everyday and professional settings.
In this talk, I will present how Bayes-optimal fusion of information from different sources can improve human–robot interaction. In particular, I will show how a robot’s uncertainty about human intentions can be reduced by optimally combining multimodal signals such as speech, gestures, and gaze. I will further present how to improve robot learning through Bayesian fusion of multimodal human advice, and how robots can detect a person’s intention to initiate interaction from multimodal cues. Across all these use cases, the proposed methods reduce uncertainty in a principled way and improve interaction quality and decision support in uncertain environments.
Bio: Susanne Trick joined the Cognitive Science Lab at TU Darmstadt in January 2019 as a PhD student. In April 2024, she successfully defended her PhD thesis with the title ‘Bayesian Fusion of Probabilistic Forecasts’ and since then is a Postdoc in the lab. From 2020 to 2024, Susanne Trick was part of the IKIDA research group investigating interactive AI algorithms and human-robot interaction. Besides the IKIDA project, from 2019 to 2021 she was also working on the Kobo34 project which aimed to contribute to maintaining the independence of elderly people with a humanoid robot that supports everyday life activities. In April 2023, Susanne Trick was awarded with the AI Newcomer Award by the German Federal Ministry of Education and Research and the German Informatics Society.
Susanne Trick conducts research at the intersection of cognitive science, machine learning, and robotics. Her research focuses on the understanding and prediction of human behavior, particularly in the interaction between human and robot. Her main interest is the integration of data from multiple modalities (e.g., gaze, gestures, speech). Consequently, she also works on probabilistic modeling of optimal combination of multimodal data, in particular considering their uncertainty and correlation.
Host: Prof. Abhinav Valada, Robot Learning Lab
