Papers
55
Total Citations
1,319
H-Index
20
About
Jivko Sinapov is a robotics and artificial intelligence researcher whose work sits at the rich intersection of robot perception, multimodal learning, and human-robot interaction. His research has fundamentally advanced how robots learn to understand and interact with the physical world — not just through vision, but through touch, sound, and proprioception. His early work on vibrotactile surface recognition (158 citations) demonstrated that robots could identify surfaces through artificial fingernails equipped with accelerometers, while his studies on acoustic object properties (58 citations) showed robots could infer material and physical characteristics purely from sound. Sinapov has made significant contributions to behavior-grounded object categorization, enabling robots to form meaningful semantic categories through exploratory interaction with objects rather than passive observation — work validated across datasets of up to 100 objects. His development of the BWIBots platform (115 citations) bridged the historically separate fields of AI and human-robot interaction research, providing a robust testbed for real-world service robotics. More recently, his research on grounded language learning and human-robot dialog (70 and 46 citations) has pushed robots toward richer, more natural communication. Across more than a decade of work, Sinapov has established himself as a pioneering voice in embodied, interactive robot learning.
Research Focus
Key Achievements
Top Papers
- 1Vibrotactile Recognition and Categorization of Surfaces by a Humanoid Robot158 citations · 2011
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- 4Learning multi-modal grounded linguistic semantics by playing I Spy70 citations · 2016
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- 6Interactive object recognition using proprioceptive and auditory feedback63 citations · 2011
- 7Interactive learning of the acoustic properties of household objects58 citations · 2009
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