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About

Jan-Gerrit Habekost is a rising researcher at the intersection of robotics, neuroscience, and artificial intelligence, whose work focuses on developing neuro-inspired control systems for collaborative humanoid robots. His primary research areas include inverse kinematics, social robotics, and sim-to-real transfer for vision-based manipulation. Habekost’s most significant contribution is the introduction of CycleIK, a novel neuro-inspired inverse kinematics approach that leverages Generative Adversarial Networks and Multi-Layer Perceptrons to solve complex motion planning tasks—a method that has already garnered 8 citations since its 2023 publication. He further advanced this work by enabling zero-shot motion planning for humanoid grasping, demonstrating how Bézier curve-based Cartesian plans can be transformed into smooth joint trajectories using CycleIK. Habekost also co-authored the design of NICOL, a neuro-inspired collaborative semi-humanoid robot that uniquely bridges social interaction and reliable manipulation, earning 20 citations. His recent work on end-to-end visuomotor architectures for sim-to-real transfer, which integrates domain adaptation as an auxiliary task, represents a promising step toward more robust and adaptable robotic systems. With a growing citation record and a clear focus on biologically-inspired solutions, Habekost is establishing himself as an innovator in neuro-robotics.