Daniel Bennequin

Délégation Paris 7, Institut de Mathématiques de Jussieu-Paris Rive Gauche, Université Paris Cité, Université Paris 8

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About

Daniel Bennequin is a mathematician and neuroscientist whose work bridges geometry, motor control, and robotics. His research centers on the geometric principles underlying human movement, particularly how the brain organizes complex motor tasks like locomotion and reaching. A major contribution is his development of task-space movement generation models that combine geometrical invariance with smoothness maximization—showing how end-effector trajectories are updated under perturbation. His most cited paper (42 citations) introduces this framework, offering insights for both human motor control and robotic planning. Bennequin has also explored the top-down organization of locomotion, demonstrating that gaze anticipation leads the body during walking, a finding that inverts conventional approaches to humanoid robot locomotion. His work on trajectory planning models (3 citations) compares human strategies for implementation on humanoid platforms. Across these studies, Bennequin applies differential geometry to understand how the nervous system solves redundancy and generates efficient, adaptive movement. His interdisciplinary approach—linking pure mathematics, experimental neuroscience, and robotics—has made him a distinctive voice in the field of motor control and geometric mechanics.