Observational Learning: Basis, Experimental Results and Models, and Implications for Robotics

TY - JOUR

T1 - Observational Learning

T2 - Basis, Experimental Results and Models, and Implications for Robotics

AU - Taylor, John G.

AU - Cutsuridis, Vassilis

AU - Hartley, Matthew

AU - Althoefer, Kaspar

AU - Nanayakkara, Thrishantha

PY - 2013/9

Y1 - 2013/9

N2 - In this paper, we describe a brief survey of observational learning, with particular emphasis on how this could impact on the use of observational learning in robots. We present a set of simulations of a neural model which fits recent experimental data and such that it leads to the basic idea that observational learning uses simulations of internal models to represent the observed activity, so allowing for efficient learning of the observed actions. We conclude with a set of recommendations as to how observational learning might most efficiently be used in developing and training robots for their variety of tasks.

AB - In this paper, we describe a brief survey of observational learning, with particular emphasis on how this could impact on the use of observational learning in robots. We present a set of simulations of a neural model which fits recent experimental data and such that it leads to the basic idea that observational learning uses simulations of internal models to represent the observed activity, so allowing for efficient learning of the observed actions. We conclude with a set of recommendations as to how observational learning might most efficiently be used in developing and training robots for their variety of tasks.

KW - Neural model

KW - Cognition

KW - Perception

KW - Action

KW - Inverse model

KW - Observational learning

KW - DARWIN robot

KW - SPECIAL-ISSUE

KW - IMITATION

KW - TRANSMISSION

KW - PERCEPTION

KW - TASK

U2 - 10.1007/s12559-013-9208-1

DO - 10.1007/s12559-013-9208-1

M3 - Article

SN - 1866-9956

VL - 5

SP - 340

EP - 354

JO - Cognitive Computation

JF - Cognitive Computation

IS - 3

M1 - N/A

ER -