Papers
10
Total Citations
189
H-Index
6
About
Xiaofeng Gao is a researcher specializing in human-robot interaction (HRI), value alignment, and collaborative AI systems. Their work sits at the intersection of cognitive science, machine learning, and robotics, with a particular focus on enabling robots to function as effective, socially intelligent teammates alongside humans. Gao's most influential contribution, "In Situ Bidirectional Human-Robot Value Alignment" (2022, 74 citations), addresses the fundamental challenge of mutual communication in human-robot teams, pioneering methods for robots to simultaneously interpret and express values in complex, multi-goal scenarios. Complementing this, their 2017 work on learning social affordance grammar from RGB-D video (39 citations) introduced a spatiotemporal AND-OR graph framework that allows humanoid robots to learn and transfer human interaction patterns in real time. Their 2020 research on joint mind modeling (37 citations) further advanced the field by enabling robots to infer human mental states — goals, beliefs, and desires — to generate meaningful explanations during collaboration. Beyond HRI, Gao has contributed to medical robotics, including flexible ureteroscopy localization and CT-based path planning. More recently, their work on language-conditioned multi-robot manipulation and embodied AI challenges reflects a growing commitment to scalable, language-aware robotic collaboration. Across these domains, Gao's research consistently pushes toward robots that are not merely functional, but genuinely communicative partners.
Research Focus
Key Achievements
Top Papers
- 1In situ bidirectional human-robot value alignment74 citations · 2022
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- 5LEMMA: Learning Language-Conditioned Multi-Robot Manipulation8 citations · 2023
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- 8Path planning of flexible ureteroscope based on CT Image4 citations · 2019
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