Ehsan Zahedi
Papers
3
Total Citations
38
H-Index
3
About
Ehsan Zahedi is a researcher at the intersection of human-robot interaction, haptic feedback, and surgical skill transfer. His work focuses on developing intelligent systems that enhance physical human-robot collaboration, particularly in high-stakes medical applications. Zahedi’s most impactful contribution is his pioneering approach to skill transfer in orthopaedic surgery, where he demonstrated how learning-based guidance can teach surgeons complex drilling techniques through haptic cues—a paper that has garnered 20 citations. He has also advanced the field of adaptive haptic guidance by comparing discriminative and generative modeling approaches, notably incorporating hidden conditional random fields to model expert gestures for more intuitive robot assistance. In earlier work, Zahedi tackled the challenge of optimizing force feedback in human-robot shared control using model predictive control, addressing the difficulty of rendering forces when human behavior is uncertain and system models are incomplete. His research bridges machine learning, control theory, and haptics, offering practical solutions for training and collaboration in medicine and beyond. With a growing citation record and a focus on translating expert knowledge into robotic systems, Zahedi is shaping the future of physically interactive robots.
Research Focus
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Top Papers
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