Max Robotics — Global Service Robot Directory + US Certification

About

Susanne Trick is a leading researcher in human-robot interaction, specializing in making assistive robots intuitive, adaptive, and collaborative for non-expert users. Her work centers on enabling robots to learn from and respond to humans in real time, with a focus on imitation learning, interactive reinforcement learning, and multimodal intention recognition. Trick’s major contributions include developing methods for robots to learn and update an open-ended library of skills on the fly, allowing them to adapt to novel, personalized tasks without pre-programming. Her highly cited 2019 paper on “Learning Intention Aware Online Adaptation of Movement Primitives” (38 citations) introduced a framework for robots to infer a human partner’s intent and adjust their movements accordingly, a critical step toward safe, fluid collaboration. She has also pioneered the use of Bayesian fusion to combine multimodal human feedback—such as speech and gestures—into reinforcement learning, significantly reducing learning times. With a growing body of work that directly addresses the challenges of deploying assistive robots in homes for elderly care, Trick’s research is foundational to creating robots that are not only intelligent but truly intuitive partners.