Susanne Trick

Susanne Trick joined the lab as a PhD student in January 2019. In April 2024, she successfully defended her PhD thesis with the title 'Bayesian Fusion of Probabilistic Forecasts' and is now 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.

• Trick, S. (2024). Bayesian Fusion of Probabilistic Forecasts (PhD thesis). [pdf]
• Trick, S.; Rothkopf, C.A.; Jäkel, F. (2023). A Normative Model for Bayesian Combination of Subjective Probability Estimates. Judgment and Decision Making, 18, E40. [pdf (wird in neuem Tab geöffnet)]
• Trick, S.; Lott, V.; Scherf, L.; Rothkopf, C.A.; Koert, D. (2023). What Can I Help You With: Towards Task-Independent Detection of Intentions for Interaction in a Human-Robot Environment. Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, Republic of Korea, pp. 592-599. [pdf]
• Trick, S.; Rothkopf, C.A.; Jäkel, F. (2023). Parameter Estimation for a Bivariate Beta Distribution with Arbitrary Beta Marginals and Positive Correlation. METRON 81, pp. 163–180. [pdf]
• Trick, S.; Herbert, F.; Rothkopf, C.A.; Koert, D. (2022). Interactive Reinforcement Learning With Bayesian Fusion of Multimodal Advice, IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7558-7565. [pdf (wird in neuem Tab geöffnet)]
• Trick, S.; Rothkopf, C. (2022). Bayesian Classifier Fusion with an Explicit Model of Correlation. Proceedings of the 2022 25th International Conference on Artificial Intelligence and Statistics (AISTATS), in Proceedings of Machine Learning Research 151, pp. 2282-2310. [pdf (wird in neuem Tab geöffnet)]
• Trick, S.; Koert, D.; Peters, J.; Rothkopf, C. (2019). Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction. Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, pp. 7009-7016. [pdf]
• Koert, D.; Trick, S., Ewerton, M.; Lutter, M.; Peters, J. (2019). Incremental Learning of an Open-Ended Collaborative Skill Library, International Journal of Humanoid Robotics.
• Koert, D.; Trick, S.; Ewerton, M.; Lutter, M.; Peters, J. (2018). Online Learning of an Open-Ended Skill Library for Collaborative Tasks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
• Koert, D.; Pajarinen, J.; Schotschneider, A.; Trick, S., Rothkopf, C.; Peters, J. (2019). Learning Intention Aware Online Adaptation of Movement Primitives, IEEE Robotics and Automation Letters (RA-L), with presentation at the IEEE International Conference on Intelligent Robots and Systems (IROS).
• Jahn, E.; Koert, D.; Trick, S.; Müller, M.; et. al (2022). Learning from Each Other—How Roboticists Learn from Users and How Users Teach Their Robots, in Meaningful Futures with Robots—Designing a New Coexistence (pp. 217-224), Chapman and Hall/CRC.