Papers
17
Total Citations
154
H-Index
8
About
Wolfram Schenck is a leading researcher in computational sensorimotor control and visuomotor coordination, with a focus on bridging biological principles and robotic systems. His work centers on learning internal models for eye-hand coordination, gaze control, and grasping—exploring how robots can adaptively predict and execute movements. A major contribution is his development of staged learning frameworks for saccadic eye movements and visuomotor transformations, as seen in his highly cited 2005 paper on learning visuomotor transformations for gaze-control and grasping (27 citations). He also pioneered spectral contrast methods for illumination-independent landmark navigation in outdoor robots (23 citations), and advanced the use of biomechanical models and sEMG signals for predicting human forearm movements in assistive devices (14 citations). His 2008 thesis on adaptive internal models for motor control and visual prediction (12 citations) synthesizes these themes, offering abstract computational models relevant to biological sensorimotor processing. With over 100 total citations across his top works, Schenck’s research has significant impact in robotics, rehabilitation, and cognitive science, demonstrating how machine learning and biomechanics can create intuitive, adaptive human-robot interaction systems.
Research Focus
Key Achievements
Top Papers
- 1Learning visuomotor transformations for gaze-control and grasping27 citations · 2005
- 2Spectral contrasts for landmark navigation23 citations · 2006
- 3Training and Application of a Visual Forward Model for a Robot Camera Head18 citations · 2008
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- 5Grasping of extrafoveal targets: A robotic model12 citations · 2009
- 6Adaptive Internal Models for Motor Control and Visual Prediction12 citations · 2008
- 7STAGED LEARNING OF SACCADIC EYE MOVEMENTS WITH A ROBOT CAMERA HEAD10 citations · 2004
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- 10Space Perception through Visuokinesthetic Prediction5 citations · 2009