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About

Yajia Zhang is a roboticist whose research centers on humanoid locomotion, motion planning, and whole-body control for complex, real-world environments. Her most significant contributions address the formidable challenge of enabling humanoid robots to climb ladders and traverse stair-like structures—a critical capability for disaster response and industrial maintenance. Zhang’s work, developed primarily on the Hubo and DRC-Hubo platforms, provides a comprehensive framework that integrates autonomous planning with compliant control to generate collision-free, stable quasi-static trajectories. Her foundational 2014 paper on robust ladder-climbing, which has garnered 33 citations, details a multi-limbed locomotion planner that automatically generates whole-body climbing motions from a simple ladder model. This work was a key component of her team’s entry in the DARPA Robotics Challenge, a high-stakes competition that pushed the boundaries of semi-autonomous humanoid operation. Beyond locomotion, Zhang has also advanced the fundamental robotics toolkit with a novel Monte Carlo technique for unbiased, scalable sampling of closed kinematic chains, a method that improves the efficiency and accuracy of motion planning for systems with loop constraints. Her research stands at the intersection of theoretical motion planning and practical, hardware-validated control.