Deep hybrid models: infer and plan in a dynamic world

TL;DR

This paper introduces a deep hybrid active inference model that integrates discrete and continuous processing to enable flexible planning and control in dynamic, hierarchical environments, demonstrated through a reaching task involving moving objects and tools.

Contribution

It presents a novel active inference framework combining hierarchical representations and trajectory inference for complex, dynamic tasks, extending planning-as-inference beyond traditional optimal control methods.

Findings

Model successfully plans and executes reaching tasks with moving objects and tools.

The approach handles dynamic and hierarchical relationships effectively.

Demonstrates flexibility and robustness in various conditions.

Abstract

To determine an optimal plan for complex tasks, 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. Here, we present an active inference approach that exploits discrete and continuous processing, based on three features: the representation of potential body configurations in relation to the objects of interest; the use of hierarchical relationships that enable the agent to easily interpret and 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.