Active inference for Robot control: A Factor Graph Approach
Active Inference provides a framework for perception,
action and learning, where the optimization is done by
minimizing the Free-Energy of a system. This paper
explores whether active inference can be used for closedloop
control of a 1 degree of freedom robot arm. This is
done by implementing variational message passing on
Forney-style factor graphs; a probabilistic programming
framework. We show that an active inference controller
with variational message passing can perform state
estimation and control at the same time.
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