Nicolas Gerig
Papers
22
Total Citations
157
H-Index
6
About
Nicolas Gerig is a robotics researcher whose work spans two interconnected domains: robot-assisted motor learning and rehabilitation, and surgical robotics for minimally invasive procedures. His early career focused on understanding how robotic systems can optimize human motor learning, with notable contributions including the development of automated feedback selection algorithms that adapt in real time to a trainee's skill level and task characteristics — work that has garnered over 28 citations. His research demonstrated that matching feedback type to individual performance profiles meaningfully accelerates skill acquisition, advancing the field beyond one-size-fits-all robotic training strategies. Gerig has since made significant strides in surgical robotics, contributing to miniature parallel robots capable of sub-millimeter laser positioning for minimally invasive osteotomy, as well as augmented and extended reality systems for surgical navigation and robot workspace visualization. His work on AR-guided orthognathic surgery and automated patient positioning reflects a commitment to translating robotic precision into real clinical workflows. With contributions spanning haptic feedback in telemanipulation, tendon-driven robotic endoscopes, and series elastic actuation, Gerig's portfolio represents a cohesive effort to make both rehabilitation and surgical robotics more intelligent, adaptive, and clinically viable.
Research Focus
Key Achievements
Top Papers
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- 10Automated Feedback Selection for Robot-Assisted Training5 citations · 2017