Matthew Miller
Papers
1
Total Citations
34
H-Index
1
About
Matthew Miller is a leading researcher in developmental robotics and cognitive systems, with a focus on how robots can learn object categories through interactive, sensorimotor experience. His key contributions lie in grounding robotic object representations in physical interaction, moving beyond static visual recognition to dynamic, behavior-based learning. His most cited work, "Toward interactive learning of object categories by a robot: A case study with container and non-container objects" (2009, 34 citations), demonstrates a pioneering approach where a robot forms categories—such as containers versus non-containers—by observing movement patterns resulting from its own actions. This work has been influential in shaping the field of interactive object learning, emphasizing that robots should not merely perceive but actively manipulate their environment to build meaningful, grounded knowledge. Miller’s research bridges artificial intelligence, psychology, and robotics, offering a framework for more adaptive and autonomous systems. His achievements include advancing the principle that sensorimotor interaction is fundamental to robust category formation, a concept with lasting impact on developmental robotics and human-robot interaction studies.
Research Focus
Key Achievements
Top Papers
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