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About

Keunjun Choi is a robotics researcher whose work bridges the gap between precise physical modeling and expressive human-robot interaction. His primary research areas include robot dynamics, model identification, and haptic interfaces for manipulation. Choi’s major contributions lie in developing advanced methods for understanding and controlling robotic systems. His most cited work, "A hybrid dynamic model for the AMBIDEX tendon-driven manipulator" (46 citations), addresses the complex dynamics of cable-driven robots. He further advanced the field with "Kinodynamic Model Identification: A Unified Geometric Approach" (32 citations), which elegantly streamlines the traditionally separate processes of kinematic and dynamic parameter estimation. Demonstrating a creative pivot, Choi’s recent work, "Towards Embedding Dynamic Personas in Interactive Robots: Masquerading Animated Social Kinematic (MASK)" (3 citations, 2024), explores embedding character-like personas into physical robots to enhance audience engagement. Additionally, his design of a proprioceptive haptic device for teaching bimanual manipulation showcases his commitment to intuitive skill transfer. Through this blend of rigorous theoretical modeling and imaginative application, Choi is shaping robots that are both precisely controlled and socially compelling.