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About

Yibin Huang is a researcher whose work spans computer vision, robotics, and autonomous systems, with a particular focus on enabling machines to perceive and act in complex, real-world environments. His most impactful contribution is in advancing object detection for autonomous driving and robotics. In his highly cited 2023 paper, "Toward RAW Object Detection: A New Benchmark and A New Model" (28 citations), Huang tackles the critical challenge of handling high dynamic range (HDR) lighting conditions—such as strong glare—by developing algorithms that work directly on RAW sensor data, bypassing traditional image processing pipelines. This work has significant implications for safety-critical applications where standard cameras fail. Beyond perception, Huang has made notable contributions to control and planning for unmanned aerial vehicles (UAVs) and mobile manipulators. His 2021 paper on "Velocity-free distributed geometric formation control for underactuated UAVs" (5 citations) introduces a novel nonlinear controller that enables multi-quadrotor systems to form and maintain formations without linear velocity feedback, a key enabler for robust swarm operations. His research also extends to medical robotics, with work on respiratory motion prediction for ultrasound-guided surgery. Huang’s diverse portfolio demonstrates a deep commitment to solving fundamental perception and control problems that bridge the gap between simulation and real-world deployment.