Papers
3
Total Citations
21
H-Index
3
About
Max Asselmeier is a leading researcher in legged robotics, specializing in perception-driven autonomy and hierarchical navigation for quadrupedal and humanoid platforms. His work centers on enabling robots to perceive and traverse complex, unstructured environments through multi-sensor integration and vision-based planning. Asselmeier’s major contributions include the development of a perception engine that fuses data from a multi-sensor head to support high-level behaviors like terrain assessment and obstacle avoidance, significantly advancing autonomous decision-making in legged robots. He also pioneered the GPF-BG framework, a hierarchical vision-based planning system that combines global path following with local Bézier curve gap navigation, achieving safe quadrupedal traversal in unknown settings. His research on hierarchical, experience-informed planning for multi-modal locomotion over constrained rebar grids demonstrates novel contact sequence optimization, enabling agile traversal of industrial environments. With papers accumulating over 20 citations, including his 2022 and 2023 works each cited 8 times, Asselmeier’s impact is evident in his integration of perception, planning, and control. His achievements have been recognized through publications in top robotics venues, and his work continues to shape the future of autonomous legged locomotion.
Research Focus
Key Achievements
Top Papers
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