Papers
19
Total Citations
209
H-Index
7
About
Thomas Schlegl’s research lies at the intersection of robotics, human-robot collaboration, and intelligent sensing, with a particular focus on creating safer, more intuitive interactions between humans and machines. His work spans from foundational contributions in dextrous robotic hand control—where he pioneered hybrid closed-loop systems for regrasping that model the hand as a discrete-continuous dynamical system—to innovative sensing technologies like “virtual whiskers” for collision avoidance and pretouch sensors that bridge the gap between vision and contact. Schlegl’s impact is evident in his highly cited work on whisker-inspired robot navigation (75 citations), which draws on biological principles to enable rapid obstacle detection. In recent years, he has advanced human-robot collaboration in industrial settings, studying user experience, motion adaptation, and gesture-based interaction to reduce training time and workload. His weakly-supervised learning approaches for multimodal activity recognition further push the boundaries of seamless teamwork. With over 180 total citations, Schlegl’s research continues to shape how robots perceive, adapt to, and safely cooperate with humans in real-world manufacturing environments.
Research Focus
Key Achievements
Top Papers
- 1Virtual whiskers — Highly responsive robot collision avoidance75 citations · 2013
- 2Hybrid closed-loop control of robotic hand regrasping32 citations · 2002
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- 6Hybrid Control of Multi-fingered Dextrous Robotic Hands9 citations · 2007
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