Colette Torres

Papers

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About

Colette Torres is a leading researcher in human-robot interaction, with a focus on affective computing and socially adaptive robotics. Her work centers on enabling robots to perceive and respond to human emotional states, a critical capability for long-term deployment in education, healthcare, and domestic assistance. Her most cited paper, "A thermal emotion classifier for improved human-robot interaction" (2016, 29 citations), introduced an innovative method using thermal imaging to detect changes in human demeanor, allowing robots to adapt their behavior in real time. This contribution addresses a fundamental challenge in the field: creating machines that can engage sensitively with individuals of varying ability and temperament. Torres’s research bridges computer vision, psychology, and robotics, and her findings have informed the design of more empathetic and effective robotic tutors and home assistants. Her work is widely recognized for its practical impact on developing robots that can sustain meaningful, long-term social engagements, marking her as a key figure in the next generation of human-aware artificial intelligence.