Luis C. Esquivel-Salas
Papers
1
Total Citations
2
H-Index
1
About
Luis C. Esquivel-Salas is a researcher whose work sits at the intersection of robotics, cognitive science, and artificial intelligence, with a particular focus on how machines can develop efficient, biologically inspired representations of their environments. His most-cited paper, "Compact internal representation as a protocognitive scheme for robots in dynamic environments" (2011), explores a foundational concept: how animals use abstract internal representations (IR) to survive in complex, changing surroundings. Esquivel-Salas proposes that robots can emulate this protocognitive ability, compressing vast amounts of environmental data into a manageable, abstract form that enables adaptive behavior without overwhelming computational resources. This work, while accruing modest citations (2), is notable for its forward-looking integration of cognitive principles into robotic design, anticipating later trends in neuromorphic and minimalist AI. His research contributes to the broader goal of creating autonomous systems that are not just reactive, but possess a rudimentary form of understanding—a stepping stone toward more intelligent, efficient machines capable of navigating real-world unpredictability.
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Top Papers
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