title: Predictive coding strategies for developmental neurorobotics.
abstract: In recent years, predictive coding strategies have been proposed as a possible means by which the brain might make sense of the truly overwhelming amount of sensory data available to the brain at any given moment of time. Instead of the raw data, the brain is hypothesized to guide its actions by assigning causal beliefs to the observed error between what it expects to happen and what actually happens. In this paper, we present a variety of developmental neurorobotics experiments in which minimalist prediction error-based encoding strategies are utilize to elucidate the emergence of infant-like behavior in humanoid robotic platforms. Our approaches will be first naively Piagian, then move onto more Vygotskian ideas. More specifically, we will investigate how simple forms of infant learning, such as motor sequence generation, object permanence, and imitation learning may arise if minimizing prediction errors are used as objective functions.
authors.affiliation: Department of Electrical Engineering, Korea Advanced Institute of Science and Technology Daejeon, Republic of Korea.
authors.forename: Jun-Cheol
authors.initials: JC
authors.lastname: Park
authors.forename: Jae Hyun
authors.initials: JH
authors.lastname: Lim
authors.forename: Hansol
authors.initials: H
authors.lastname: Choi
authors.forename: Dae-Shik
authors.initials: DS
authors.lastname: Kim