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About

Stephen D. Miller is a leading figure in robotic manipulation, whose work has fundamentally advanced how robots handle deformable objects—particularly textiles. His research centers on perception, manipulation planning, and machine learning for tasks involving cloth and other non-rigid materials. Miller’s most influential contribution is his geometric approach to robotic laundry folding, which introduced a quasi-static cloth model that bypasses the complex dynamics of fabric, enabling practical autonomous folding (252 citations). He further demonstrated superhuman performance in surgical subtasks through iterative learning from human demonstrations (243 citations), showing that robots can exceed human precision in delicate operations. His work on bringing clothing into desired configurations using hidden Markov models (153 citations) and textured object recognition pipelines (117 citations) has set benchmarks in perception for household robotics. Miller’s achievements include pioneering the manipulation of socks and other challenging textile items, addressing perceptual problems that were previously considered intractable. With over 800 citations across his top papers, Miller’s research has had a profound impact on both domestic robotics and surgical assistance, inspiring a generation of researchers to tackle the frontier of deformable object manipulation.