Michael Sheldon
Aberystwyth University, Rutgers, The State University of New Jersey
Papers
8
Total Citations
154
H-Index
6
About
Michael Sheldon is a pioneering researcher in developmental cognitive robotics, whose work draws deep inspiration from infant psychology and sensorimotor learning. His primary research areas include autonomous skill acquisition, coordinated reaching, and the emergence of communication in embodied agents. Sheldon’s major contribution lies in modeling how robots can learn complex behaviors—such as visually guided reaching and grasping—through developmental progression, without requiring pre-programmed task knowledge. His 2014 paper on a psychology-based approach for longitudinal development in cognitive robotics (60 citations) is his most influential, establishing a framework for robots to learn novel goals and skills solely through environmental interaction. He also introduced the PSchema framework (2011), a Piagetian-inspired schema learning system that enables symbolic learning in robotic environments. Beyond robotics, Sheldon contributed to the coordination of large-scale human induced pluripotent stem cell initiatives (38 citations), demonstrating interdisciplinary breadth. His work on synergy-based affordance learning and infant-inspired reaching models has advanced the understanding of how robots can replicate human-like motor development. Sheldon’s research is essential reading for those interested in autonomous learning, developmental robotics, and bio-inspired artificial intelligence.
Research Focus
Key Achievements
Top Papers
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- 4PSchema: A developmental schema learning framework for embodied agents10 citations · 2011
- 5An infant inspired model of reaching for a humanoid robot6 citations · 2012
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- 7Synergy-based affordance learning for robotic grasping5 citations · 2013
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