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About

Joni Dambre is a researcher at the intersection of robotics, machine learning, and neural computation, with particular expertise in compliant robotics, locomotion control, and human-robot interaction. Her work spans both fundamental algorithmic development and applied robotics systems, making significant contributions to how robots learn, move, and interact with humans. Among her most influential contributions is a differentiable physics engine for deep learning in robotics (2019, 176 citations), which introduced gradient-based optimization as a powerful alternative to derivative-free methods like evolutionary algorithms and reinforcement learning for controller design. This work helped open robotics systems to more efficient, scalable learning pipelines. Dambre has also pioneered research into compliant and soft robotics, investigating how the physical properties of robot bodies — explored through mass-spring networks and quadrupedal platforms — can simplify locomotion control through morphological computation and embodied learning principles. Beyond locomotion, she has made meaningful contributions to socially assistive robotics, particularly in applications for children with autism and cognitive impairments (107 citations), demonstrating a commitment to translating robotics research into human-centered settings. Her body of work reflects a rare breadth — uniting theoretical rigor in physics-based learning with compassionate, applied research — making her a distinctive voice in modern robotics and AI.