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About

Qianli Xu is a robotics and human-robot interaction (HRI) researcher whose work spans social robotics, active robot perception, and multimodal manipulation. With a body of research accumulating over 180 citations, Xu has made meaningful contributions across two complementary fronts: understanding how humans perceive and interact with robots, and advancing the cognitive and perceptual capabilities of robotic systems themselves. Early in his career, Xu tackled foundational HRI challenges, examining how scenario presentation media influence user evaluations (2012), how robots can select addressees when interacting with multiple people (2012), and how robot appearance shapes perceived personality (2012). His 2014 work on methodological issues in scenario-based HRI evaluation—his most cited paper with 41 citations—remains a valuable reference for researchers designing user studies. He also investigated Singaporean attitudes toward home service robots across generations, reflecting a commitment to socially grounded robotics research. More recently, Xu has pivoted toward machine learning-driven robot perception, contributing self-supervised reinforcement learning methods for active object detection, adaptive multiview planning, and gesture-enhanced instruction comprehension. His 2023 visuo-tactile manipulation work demonstrates a sophisticated integration of multiple sensory modalities for challenging real-world packing tasks. Together, these contributions position Xu as a versatile researcher bridging the human and technical dimensions of modern robotics.