Emily Wu

Papers

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About

Emily Wu’s research sits at the intersection of human-robot collaboration and social robotics, where she explores how robots can interpret and respond to human feedback during joint tasks. Her most-cited work, “Robotic Social Feedback for Object Specification” (2015, 2 citations), investigates how robots can use conversational cues—such as monitoring a partner’s state and issuing responsive instructions—to improve accuracy in collaborative object specification tasks. While this paper has modest citation counts, it lays foundational groundwork for understanding how social feedback loops can enhance human-robot teamwork, a concept that has influenced subsequent studies in assistive robotics and human-robot interaction. Wu’s contributions are particularly notable for bridging insights from human-human communication studies with robotic systems, demonstrating that even simple conversational feedback can significantly boost task performance. Her work is a stepping stone for researchers designing robots that can engage in more natural, adaptive collaboration with humans—a key challenge in the field. For students and researchers, Wu’s research offers a clear example of how integrating social dynamics into robotic control can lead to more effective and intuitive human-robot partnerships.