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About

Ha Thang Long Doan is a roboticist whose research focuses on advancing the capabilities of under-actuated robotic hands—a class of grippers that trade mechanical complexity for adaptability. His core contributions lie in solving two fundamental challenges for these systems: detecting fingertip contact with objects and estimating the forces applied during grasping, all without relying on expensive or fragile tactile sensors. Doan’s work introduces data-driven and clustering-based methods to overcome the nonlinear dynamics and self-locking mechanisms inherent in under-actuated designs. In his most-cited paper (2022, 4 citations), he developed a density-based clustering approach for contact detection. He extended this in 2024 (3 citations) to achieve sensor-less force estimation, and in 2023 (3 citations) he proposed a data-driven framework for stable precision grasping. His 2023 study on in-hand object manipulation (2 citations) further demonstrates how to compensate for mechanical nonlinearities to enable dexterous control. Though early in his career, Doan’s systematic approach to making under-actuated hands more reliable and sensor-efficient is paving the way for practical, low-cost robotic manipulation in industrial and assistive applications.