Simon Hagenmayer
Papers
1
Total Citations
4
H-Index
1
About
Simon Hagenmayer is a roboticist whose research lies at the intersection of human-robot interaction, task and motion planning (TAMP), and machine learning. His most-cited work, “Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks” (2021), introduces a novel framework that fuses hierarchical human motion prediction with logic-geometric programming. By combining Inverse Reinforcement Learning with TAMP, Hagenmayer enables robots to anticipate human actions and plan their own motions to minimize interference during collaborative manipulation tasks. This work, with 4 citations, is foundational for developing robots that can work fluidly alongside humans in shared spaces, such as assembly lines or domestic environments. His contributions are particularly notable for addressing the challenge of real-time coordination, where robots must not only predict but also adapt their behavior to human intentions. Hagenmayer’s research is paving the way for safer, more intuitive human-robot collaboration, and his approach has implications for assistive robotics, manufacturing, and autonomous systems. As a rising scholar, his work is gaining traction among researchers focused on integrating cognitive models with robotic control.
Research Focus
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Top Papers
- 1