Susanne Trick

Susanne Trick joined the lab as a PhD student in January 2019.

She is 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 aims to contribute to maintaining the independence of elderly people with a humanoid robot that supports everyday life activities.

Susanne Trick’s 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.; 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, July 2022, doi: 10.1109/LRA.2022.3182100.[pdf (opens in new tab) ]
• Trick, S.; Rothkopf, C. (2022). Bayesian Classifier Fusion with an Explicit Model of Correlation. In International Conference on Artificial Intelligence and Statistics. [pdf (opens in new tab)]
• Trick, S.; Koert, D.; Peters, J.; Rothkopf, C. (2019). Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
• 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.