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About

Zhao-Heng Yin is a robotics researcher specializing in dexterous manipulation, tactile sensing, and imitation learning for robotic hands. His work sits at the intersection of robot learning and sensorimotor integration, with a particular focus on enabling multi-finger robot hands to achieve human-level dexterity. Yin's most influential contribution, "Rotating without Seeing: Towards In-hand Dexterity through Touch" (2023, 70 citations), demonstrated that robotic hands could perform complex in-hand object rotation using tactile feedback alone — mirroring a capability long considered uniquely human. This work established him as a leading voice in touch-driven robot dexterity. He extended this vision in "Robot Synesthesia" (2024, 32 citations), developing systems that fuse visual and tactile modalities to tackle contact-rich manipulation tasks, drawing an evocative parallel to cross-sensory perception in biological systems. Beyond sensing, Yin has explored cross-domain imitation learning and, most recently, ultrafast neural hand retargeting through his GeoRT algorithm, capable of converting human finger keypoints to robot configurations at 1kHz. Together, his body of work advances a cohesive agenda: bringing human-like sensorimotor intelligence to robotic manipulation systems.