Papers
8
Total Citations
74
H-Index
5
About
Andre Meixner is a leading researcher at the intersection of robotics, human motion analysis, and cognitive architectures. His work focuses on enabling robots to perform complex, human-like manipulation tasks through the principled application of Riemannian geometry and machine learning. Meixner’s major contributions include developing a memory system for the ArmarX robot cognitive architecture, which provides a foundational framework for long-term robot autonomy. He has pioneered the use of Riemannian manifolds for human motion analysis and retargeting, allowing for the generation of dynamic, posture-dependent robot motions that closely mimic human movement. His research also extends to the automated design of simple yet robust manipulators for dexterous in-hand manipulation, demonstrating that task-specific morphology optimization can yield low-cost, highly capable hands. With over 70 citations across his most-cited works, Meixner’s impact is evident in his systematic approach to unifying human likeness metrics and developing collision-safe motion generation techniques. Notably, his work on the euROBIN robotics hackathon showcases his ability to lead complex, multi-robot systems for real-world logistics tasks. Meixner’s research is shaping the future of intuitive human-robot interaction and dexterous manipulation.
Research Focus
Key Achievements
Top Papers
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- 2A Riemannian Take on Human Motion Analysis and Retargeting17 citations · 2022
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