Imitation in animals and artifacts
Empirical estimates of adaptation: the chance of two noriegas is closer to p/2 than p2
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
Learning object-manipulation verbs for human-robot communication
Proceedings of the 2007 workshop on Multimodal interfaces in semantic interaction
Acquisition of Human-Robot Interaction Rules via Imitation and Response Observation
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
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This paper presents a novel computational model of role reversal imitation in continuous human-robot interaction. In role reversal imitation, a learner not only imitates what a tutor does, but also takes the tutor's role and performs the tutor's teaching actions to check that the appropriate response is elicited. The learning architecture mainly consists of three learning modules: the switching autoregressive model (SARM), keyword extractor without a dictionary, and keyword selection filter that refers to the tutor's reactions. To imitate certain behaviors from the continuous motion of a person, a robot must find segments that should be learned. To achieve this goal, the learning architecture converts the continuous time series into a discrete time series of letters by using SARM, finds meaningful segments by using the keyword extractor without a dictionary, and removes not so meaningful segments from the keywords by utilizing its user's reactions. An experiment was performed in a low-dimensional world, and the results show that the framework enabled a robot to obtain several meaningful motions that the experimenter wanted the robot to acquire.