A generative model for developmental understanding of visuomotor experience

By manipulating objects in their environment, infants learn about the surrounding environment and continuously improve their internal model of their own body. Moreover, infants learn to distinguish parts of their own body from other objects in the environment. In the field of neuroscience, studies have revealed that the posterior parietal cortex of the primate brain is involved in the awareness of self-generated movements. In the field of robotics, however, little has been done to propose computationally reasonable models to explain these biological findings. In the present paper, we propose a generative model by which an agent can estimate appearance as well as motion models from its visuomotor experience through Bayesian inference. By introducing a factorial representation, we show that multiple objects can be segmented from an unsupervised sensory-motor sequence, single frames of which appear as a random patterns of dots. Moreover, we propose a novel approach by which to identify an object associated with self-generating action.