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We show that goal-directed action planning and generation in a teleologicalframework can be formulated using the free energy principle. The proposedmodel, which is built on a variational recurrent neural network model, ischaracterized by three essential features. These are that (1) goals can bespecified for both static sensory states, e.g., for goal images to be reachedand dynamic processes, e.g., for moving around an object, (2) the model can notonly generate goal-directed action plans, but can also understand goals bysensory observation, and (3) the model generates future action plans for givengoals based on the best estimate of the current state, inferred using pastsensory observations. The proposed model is evaluated by conducting experimentson a simulated mobile agent as well as on a real humanoid robot performingobject manipulation.

Goal-directed Planning and Goal Understanding by Active Inference: Evaluation Through Simulated and Physical Robot Experiments