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About

Philipp Kratzer is a robotics researcher whose work sits at the intersection of human-robot interaction, motion prediction, and collaborative task planning. His research addresses one of the central challenges in modern robotics: enabling robots to work alongside humans in natural, intuitive, and safe ways across diverse real-world environments. Kratzer's most influential contribution examines how humans prefer to interact with collaborative robots depending on the type of task being performed, a 2018 study that has garnered 30 citations and highlights the importance of adaptive interaction design in both industrial and domestic settings. His complementary work on full-body human motion prediction—including the development of MoGaze, a dataset uniquely combining motion capture with eye-gaze and workspace geometry data—advances the field's ability to anticipate human intent in unstructured environments. More recently, his 2021 work integrating hierarchical motion prediction with Task and Motion Planning represents a sophisticated step toward robots capable of minimizing interference during complex manipulation sequences. Across his portfolio, Kratzer demonstrates a consistent focus on bridging perception, prediction, and planning to make human-robot collaboration genuinely seamless. His work is essential reading for students exploring cognitive robotics and collaborative AI systems.