Papers
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About
Dan Xie is a researcher whose work lies at the intersection of human-robot interaction, multi-agent systems, and cooperative control. His key research areas focus on developing algorithms that enable robots to understand and anticipate human intentions, particularly in dynamic, collaborative environments. Xie’s major contribution is his pioneering framework for intention-based coordination, which uses probabilistic models—such as stochastic activity recognition—to allow robots to infer human goals during cooperative tasks like search and rescue. This approach bridges the gap between human cognitive strategies and robotic autonomy, making human-robot teams more efficient and intuitive. His most cited work, "Intention-based coordination and interface design for human-robot cooperative search" (2011), has garnered 2 citations and lays the groundwork for adaptive human-robot interfaces. Though early in its citation impact, this paper is notable for its forward-thinking integration of machine learning with real-time human state estimation. Xie’s research is particularly relevant for students and engineers interested in creating robots that can seamlessly collaborate with people, offering a foundational perspective on how to design systems that are both perceptive and responsive to human intent.
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Top Papers
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