Cognitive Robotics, Enactive Perception, and Learning in the Real World

Abstract

Robotic cognitive modeling in the real world requires a level of modeling. However, like Newell we believe this is exactly the kind of integration needed to promote scientific cumulation in the cognitive sciences. We present a neural model of learning compatible with Noë’s account of enactive perception. We highlight that accounts of enactive perception tend to oversimplify the problem of identifying contingent relationships and introduce a novel way to address the problem of marginal regularities. Finally, we describe a general (non-task specific) model and present a number of real-world robotic experiments demonstrating a wide range of integrated psychological phenomena.