Stephanie Gross
Papers
8
Total Citations
83
H-Index
5
About
Stephanie Gross is a leading researcher at the intersection of human-robot interaction (HRI), situated communication, and grounded language learning. Her work centers on understanding how humans and robots can collaborate naturally through verbal and non-verbal cues, particularly in industrial and task-oriented settings. Gross’s major contributions include mapping communicative modes onto collaborative scenarios, as detailed in her most-cited paper, “A Communicative Perspective on Human–Robot Collaboration in Industry” (28 citations). She pioneered the use of virtual reality environments, such as CoBot Studio VR, to study interpretability and trust in human-robot teams. Her research on the reliability of non-verbal cues for reference resolution (13 citations) and transparency methods in robot word-learning systems (11 citations) has advanced how robots learn from human tutors. Gross has demonstrated real-time grounded word learning on the Pepper robot, enabling it to map words to objects and actions from cross-modal data. Her work is notable for bridging cognitive science and robotics, with applications in collaborative pick-and-place scenarios and industrial automation. With a growing citation record, Gross is shaping the future of intuitive, trustworthy human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 5Grounded Word Learning on a Pepper Robot7 citations · 2018
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