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About

Chenyu Yang is a robotics researcher whose work bridges agricultural automation and legged manipulation, with a focus on enabling robots to operate intelligently in unstructured environments. His key research areas include autonomous navigation for agricultural robots, machine vision, and dynamic control for quadrupedal systems. Yang’s major contributions include developing an end-to-end learning-based row-following system for agricultural robots in structured apple orchards, which maps camera images directly to driving commands, bypassing traditional subtask decomposition. He also designed a complete navigation system for a banana-picking robot, addressing the critical challenge of headland turning control through machine vision. Beyond agriculture, Yang has advanced legged robotics by proposing a model predictive control (MPC) framework for quadrupedal robots to dynamically balance and manipulate objects—such as a ball—through multi-contact optimization, showcasing the untapped potential of using robot feet for manipulation tasks. His most-cited work, on row-end detection for banana-picking robots, has garnered 20 citations, reflecting its practical impact on precision agriculture. Yang’s interdisciplinary approach, combining learning-based perception with dynamic control, positions him as a rising innovator in both field robotics and legged manipulation.