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About

Haoxiang Li is a robotics researcher whose work sits at the intersection of computer vision, autonomous navigation, and human-robot interaction. His primary contributions focus on developing intelligent, vision-driven systems that enable robots and assistive devices to perceive, navigate, and interact with their environments with minimal human intervention. Li is perhaps best known for his work on egocentric computer vision for co-robot wheelchairs, a line of research that has garnered over 28 citations and aims to restore mobility to individuals with limited hand functionality through hands-free, vision-based control. His most cited paper, "Active Object Perceiver" (53 citations), introduces a novel reinforcement learning framework for mobile robots to actively search for objects in indoor spaces, blending scene understanding with policy learning. Li has also explored creative applications of robotics, such as the LeRoP framework for automatic portrait photography, and has tackled safety in reinforcement learning through supervised training methods. His recent work on lightweight neural networks for mobile robot tracking reflects a continued commitment to deploying efficient, real-time vision systems on resource-constrained platforms.