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About

Carlos Calvo investigates how biological and artificial agents navigate, learn, and interact in complex, dynamic environments. His research bridges cognitive science, robotics, and complex systems, focusing on the mechanisms underlying anticipation, social learning, and semantic reasoning. Calvo’s most influential work introduces “Prediction-for-CompAction,” a framework using generalized cognitive maps for navigation in social settings (26 citations). He has also pioneered the use of isotropic totalistic cellular automata for real-time robot navigation, overcoming the anisotropy that plagues traditional models (15 citations). Further contributions include a novel approach to semantic knowledge representation for strategic interactions (13 citations) and a motor-motif-based model of fast social-like learning (12 citations). His work on limb movement in dynamic situations (10 citations) abstracts cognitive control for object manipulation. Collectively, Calvo’s research offers computationally efficient, brain-inspired solutions for autonomous systems, with applications ranging from robotics to understanding biological cognition. His papers, though modestly cited individually, represent a coherent and innovative program that advances the frontier of embodied intelligence and situated decision-making.