Papers
6
Total Citations
90
H-Index
5
About
Andrew Choi is a robotics and computational mechanics researcher whose work sits at the intersection of deformable object manipulation, physics-based simulation, and sim-to-real transfer for robotic systems. His research addresses one of robotics' most persistent challenges: enabling machines to reliably perceive, model, and manipulate flexible structures such as cables, rods, and paper in real-world settings. Choi's most recognized contribution, mBEST (25 citations), introduced a real-time detection algorithm for deformable linear objects using minimal bending energy skeleton traversals, providing a practical visual feedback solution for robotic manipulation pipelines. Complementing this, his sim2real neural controller framework (18 citations) and neural force manifold approach for paper folding (11 citations) demonstrate his ability to bridge the gap between simulation and physical deployment for highly nonlinear deformable systems. His DisMech simulator (16 citations) offers the robotics community a fast, accurate, and generalizable tool grounded in Discrete Differential Geometry (DDG) — a mathematically principled framework he further champions through a comprehensive survey (2025). Additional work on bio-inspired soft actuators (16 citations) reflects his broader engagement with soft robotics. Across his portfolio, Choi is establishing himself as a distinctive voice in physics-informed, simulation-driven approaches to flexible-body robotics.
Research Focus
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Top Papers
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