Bayesian Clustering of Sensory Inputs by Dynamics


This paper describes a Bayesian approach to the abstraction of sensor dynamics using a new clustering algorithm for time series to learn prototypical behaviors of a robot's sensory inputs. Each sensor stream reading is modeled as a Markov chain (mc). The abstraction process is performed by an unsupervised clustering algorithm returning the most probable set… (More)


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