Papers
7
Total Citations
115
H-Index
4
About
Alexander V. Terekhov is a researcher whose work bridges robotics, sensorimotor control, and machine learning. His key research areas include proprioceptive sensing, robot kinematics learning, and aggressive maneuver control for mobile robots. One of his most significant contributions is the discovery that changes in fingertip contact area serve as a novel proprioceptive cue, a finding published in 2016 that has garnered 73 citations and reshaped understanding of haptic perception. Terekhov also pioneered methods for learning a robot’s spatial configuration from sensorimotor invariants (22 citations) and for automatically discovering forward kinematics without prior structural knowledge (10 citations), advancing autonomous robot adaptation. In applied robotics, he developed stochastic optimization techniques for neural network-based controllers enabling aggressive 90-degree turns at high speeds (10 m/s) on loose surfaces, with experimental validation of drift maneuvers. His work on chain sliding mode controllers further enhanced robustness on slippery terrain. Terekhov’s research has practical implications for autonomous navigation and human-robot interaction, demonstrating a unique blend of theoretical insight and experimental rigor.
Research Focus
Key Achievements
Top Papers
- 1The Change in Fingertip Contact Area as a Novel Proprioceptive Cue73 citations · 2016
- 2Learning agent’s spatial configuration from sensorimotor invariants22 citations · 2015
- 3Learning an internal representation of the end-effector configuration space10 citations · 2013
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- 6EXPERIMENTAL STUDY OF A FAST MOBILE ROBOT PERFORMING A DRIFT MANEUVER2 citations · 2010
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