Papers
23
Total Citations
450
H-Index
14
About
Fangkai Yang is a robotics and AI researcher whose work sits at the intersection of autonomous planning, human-robot interaction, and machine learning. His research spans task-motion planning, reinforcement learning for mobile service robots, and socially intelligent robot behavior — areas in which he has made notable and lasting contributions. Yang's most influential work introduced the BWIBots platform (115 citations), a multi-robot system designed to bridge the gap between AI research and real-world human-robot interaction, enabling robots to execute complex service tasks in open environments. Building on this foundation, he advanced task-motion planning by integrating reinforcement learning to allow robots to adaptively learn action costs in continuous spaces, while his earlier work on Answer Set Programming demonstrated elegant solutions to planning under incomplete information. A distinctive thread in Yang's research is his focus on social robotics: he has applied deep reinforcement learning and generative adversarial networks to teach robots how to approach human groups in socially appropriate ways, and explored trust and interpretability in human-robot decision-making through his TDM framework. His work on mixed reality further extends his interest in how humans perceive and interact with artificial agents. Collectively, his publications reflect a researcher committed to making robots not only capable, but genuinely safe and socially aware.
Research Focus
Key Achievements
Top Papers
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- 3Planning in Action Language BC while Learning Action Costs for Mobile Robots31 citations · 2014
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- 6Ontology Based Object Categorization for Robots24 citations · 2008
- 7TDM: Trustworthy Decision-Making Via Interpretability Enhancement21 citations · 2021
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- 9Planning with task-oriented knowledge acquisition for a service robot19 citations · 2016
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