Michael C. Hughes
Papers
2
Total Citations
17
H-Index
2
About
Michael C. Hughes is a leading researcher in cognitive robotics and sensorimotor learning, with a focus on how robots can acquire grounded object knowledge through active exploration. His work bridges the gap between human developmental learning and artificial intelligence, investigating how robots can leverage multi-modal perception—touch, sound, and vision—to categorize objects without relying solely on visual data. Hughes’s most influential papers, including “A Framework for Sensorimotor Cross-Perception and Cross-Behavior Knowledge Transfer for Object Categorization” (2020, 9 citations) and “Sensorimotor Cross-Behavior Knowledge Transfer for Grounded Category Recognition” (2019, 8 citations), introduce novel frameworks for transferring knowledge across different exploratory behaviors, such as grasping, pushing, and shaking. These contributions enable robots to infer physical properties like weight, texture, and material from action-driven observations, significantly advancing the field of developmental robotics. By demonstrating that robots can learn intuitive physics similar to human infants, Hughes’s work has opened new pathways for creating more adaptable and intelligent autonomous systems. His research is particularly impactful for students and researchers interested in embodied cognition, multi-modal learning, and the future of human-robot interaction.
Research Focus
Key Achievements
Top Papers
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