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We propose a hybrid combination of active inference and behavior trees (BTs)for reactive action planning and execution in dynamic environments, showing howrobotic tasks can be formulated as a free-energy minimization problem. Theproposed approach allows handling partially observable initial states andimproves the robustness of classical BTs against unexpected contingencies whileat the same time reducing the number of nodes in a tree. In this work, wespecify the nominal behavior offline, through BTs. However, in contrast toprevious approaches, we introduce a new type of leaf node to specify thedesired state to be achieved rather than an action to execute. The decision ofwhich action to execute to reach the desired state is performed online throughactive inference. This results in continual online planning and hierarchicaldeliberation. By doing so, an agent can follow a predefined offline plan whilestill keeping the ability to locally adapt and take autonomous decisions atruntime, respecting safety constraints. We provide proof of convergence androbustness analysis, and we validate our method in two different mobilemanipulators performing similar tasks, both in a simulated and real retailenvironment. The results showed improved runtime adaptability with a fractionof the hand-coded nodes compared to classical BTs.

Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics