Active inference for Robot control: A Factor Graph Approach

Active Inference provides a framework for perception,action and learning, where the optimization is done byminimizing the Free-Energy of a system. This paperexplores whether active inference can be used for closedloopcontrol of a 1 degree of freedom robot arm. This isdone by implementing variational message passing onForney-style factor graphs; a probabilistic programmingframework. We show that an active inference controllerwith variational message passing can perform stateestimation and control at the same time