Deep hybrid models: infer and plan in a dynamic world

In order to determine an optimal plan for a complex task, one often deals with dynamic and hierarchical relationships between several entities. Traditionally, such problems are tackled with optimal control, which relies on the optimization of cost functions; instead, a recent biologically-motivated proposal casts planning and control as an inference process. Active inference assumes that action and perception are two complementary aspects of life whereby the role of the former is to fulfill the predictions inferred by the latter. In this study, we present a solution, based on active inference, for complex control tasks. The proposed architecture exploits hybrid (discrete and continuous) processing, and it is based on three features: the representation of potential body configurations related to the objects of interest; the use of hierarchical relationships that enable the agent to flexibly expand its body schema for tool use; the definition of potential trajectories related to the agent's intentions, used to infer and plan with dynamic elements at different temporal scales. We evaluate this deep hybrid model on a habitual task: reaching a moving object after having picked a moving tool. We show that the model can tackle the presented task under different conditions. This study extends past work on planning as inference and advances an alternative direction to optimal control.