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About

Yusheng Yang is a robotics researcher whose work bridges path planning, biomimetic perception, and geometric deep learning. His primary research areas include real-time obstacle avoidance for high-degree-of-freedom manipulators, stereo-vision-based human-robot coordination, and novel neural representations for robotic morphology. Yang’s most cited work, “Improved Distorted Configuration Space Path Planning and Its Application to Robot Manipulators” (2020, 12 citations), tackles the computational bottleneck of planning in crowded workspaces by introducing a distorted configuration space strategy that enables efficient real-time collision avoidance for high-DOF robots. In “Research on Biomimetic Coordination Action of Service Robot Based on Stereo Vision” (2018, 8 citations), he applied bionic stereoscopic vision and the Goodman model to design a home service robot capable of human-like coordination. More recently, Yang has advanced geometric deep learning with “UVS-CNNs” (2024), constructing general CNNs on quasi-uniform spherical images, and “RobotSDF” (2024), which uses implicit neural representations to model robotic arm morphology for efficient motion planning. His work consistently addresses the trade-off between computational efficiency and expressive power, making contributions that are relevant to both industrial manipulation and service robotics.