Abstract
Besides considering human-robot body experience as a metric in robot and control design, understanding it in a broader sense and context is of psychological and technical interest. This chapter discusses the potential of cognitive models of body experience in robotics. Approaches like Bayesian or connectionist models might enable adapting assistive robots to their users’ body experiences or to endowed humanoid robots with human-like body representations. Besides improving interaction capabilities, this might yield human-like action-perception versatility. However, cognitive body experience models do not yet achieve sufficient accuracy, individualization, and online capabilities. As an exemplary approach, a Bayesian cognitive model of crossmodal sensory processing during the rubber foot illusion is presented. Estimations of empirical results with an extended Bayesian causal inference model are improved by involving empirically informed prior information.
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