Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Incremental learning of integrated semiotics based on linguistic and behavioral symbols
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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This paper describes the linguistic model based on symbolization of motion patterns for humanoid robots. The model consists of two kinds of stochastic models: the motion language model and the natural language model. The motion language model stochastically connects the symbols of motion patterns to the morpheme words through the latent states which represent the underlying linguistic structure such as semantic contents. The natural language model represents the dynamics of the word classes. The motion language model and the natural language model correspond to semantics and syntax respectively. The integration of the motion language model and the natural language model allows robots not only to linguistically interpret the motion patterns as sentences but also to generate the motions from the sentences. The two kinds of linguistic processes of the interpretation and the generation can be obtained by solving search problems: search for a sequence of morpheme words and a symbol of motion pattern. The proposed approach to interpretation of motion patterns as sentences and generation of motion patterns from the sentences through integration of the motion language model and the natural language model is validated by the experiment on the human behavioral data.