Papers
12
Total Citations
360
H-Index
9
About
Vivian Chu is a pioneering researcher in human-robot interaction and robotic learning, whose work focuses on enabling robots to understand and communicate through touch and natural language. Her most influential contributions center on teaching robots to learn haptic adjectives—words like "soft," "hard," or "rough"—through direct physical interaction, as demonstrated in her highly cited papers "Robotic learning of haptic adjectives through physical interaction" (145 citations) and "Using robotic exploratory procedures to learn the meaning of haptic adjectives" (107 citations). These works lay the foundation for robots that can develop semantic understanding by touching objects, bridging the gap between raw sensor data and human-like language. Chu has also advanced object affordance learning, showing how robots can combine human guidance with self-exploration to efficiently acquire manipulation skills. Her Situated Bayesian Reasoning Framework (2019) addresses how robots can reason adaptively in diverse everyday environments. With a total of over 350 citations across her top papers, Chu’s research is instrumental in delivering on the promise of real-world robotics—creating machines that learn from experience and communicate naturally with people.
Research Focus
Key Achievements
Top Papers
- 1Robotic learning of haptic adjectives through physical interaction145 citations · 2014
- 2Using robotic exploratory procedures to learn the meaning of haptic adjectives107 citations · 2013
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- 5Multimodal real-time contingency detection for HRI12 citations · 2014
- 6Incremental Task Modification via Corrective Demonstrations12 citations · 2018
- 7Learning haptic affordances from demonstration and human-guided exploration10 citations · 2016
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