Papers
9
Total Citations
406
H-Index
8
About
Paul Ruvolo is a leading researcher at the intersection of machine learning, developmental psychology, and social robotics. His work is defined by a unique dual focus: advancing the algorithmic foundations of efficient learning in machines while simultaneously using robots as tools to understand fundamental aspects of human social interaction. In machine learning, he is best known for his highly cited work on online multi-task learning for policy gradient methods (149 citations), which significantly improved the sample efficiency of reinforcement learning for high-dimensional robotic control. On the human-robot interaction side, his groundbreaking study on infant-mother smiling dynamics (91 citations) applied computational modeling to reveal that infants strategically time their smiles to maximize their mothers' responses. He also demonstrated that humans spontaneously mimic the facial expressions of physically present androids (60 citations), a key finding for understanding empathy and social bonding with machines. A core contributor to the RUBI project, Ruvolo has also developed practical tools for social robots, including auditory mood detection and apprenticeship learning for teaching. His work bridges the mechanical and the human mind, offering profound insights into how we learn, interact, and connect.
Research Focus
Key Achievements
Top Papers
- 1Online Multi-Task Learning for Policy Gradient Methods149 citations · 2014
- 2Infants Time Their Smiles to Make Their Moms Smile91 citations · 2015
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- 4The RUBI project36 citations · 2007
- 5Auditory mood detection for social and educational robots21 citations · 2008
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- 8Building a more effective teaching robot using apprenticeship learning9 citations · 2008
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