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About

Xiaozhu Ju is a robotics researcher whose work spans two compelling frontiers: advanced whole-body control (WBC) for humanoid and legged robots, and large-scale benchmarking for embodied intelligence. His foundational contributions to WBC address critical challenges in multi-task robot behavior, including pioneering a recursive hierarchical projection framework for smooth task priority transitions — ensuring robots can fluidly switch objectives without control discontinuities. His work on mixed control strategies for humanoid compliance tackles instability issues inherent in hierarchical quadratic programming, while his investigations into parallel-legged robots introduce motion/force transmissibility as a novel performance consideration within WBC frameworks. More recently, Ju has made a significant mark on the data-driven robotics landscape through RoboMIND, an ambitious benchmark dataset comprising over 107,000 demonstration trajectories spanning 479 tasks and 96 object classes, collected via human teleoperation across multiple robot embodiments. This work, already accumulating 14 citations shortly after its 2025 publication, positions Ju at the intersection of control theory and robot learning. His research collectively advances the field's understanding of how robots can operate compliantly, adaptively, and intelligently across diverse real-world scenarios.