This paper presents how the hybrid combination of behavior trees and the neuroscientific principle of active inference can be used for action planning and execution for reactive robot behaviors in dynamic environments. We show how complex robotic tasks can be formulated as a free-energy minimisation problem, and how state estimation and symbolic decision making are handled within the same framework. The general behavior is specified offline through behavior trees, where the leaf nodes represent desired states, not actions  as in classical behavior trees . The decision of which action to execute to reach a state is left to the online active inference routine, in order to resolve unexpected contingencies. This hybrid combination improves the robustness of plans specified through behavior trees, while allowing to cope with the curse of dimensionality in active inference . The properties of the proposed algorithmare analysed in terms of robustness and convergence , and the theoretical results are validated using a mobile manipulator in a retail environment.
We propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments. We show how robotic tasks can be formulated as a free-energy minimization problem, bringing the neuroscientific theory of active inference on a mobile manipulator. In this framework, the general nominal behavior is specified offline through BTs. A new type of leaf node, theprior node, is introduced to specify the desired state to be achieved rather than an action to be executed as in classical BTs . The decision of which action to execute to reach the desired state is performed online through active inference. The resulting hybrid combination improves the robustness of plans against unexpected contingencies while considerably reducing the number of nodes in a BT . The properties of our algorithm, such as the convergence and robustness, are thoroughly analyzed and outperform classical BT solutions. The theoretical results are validated using a mobile manipulator in a retail environment.
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This paper presents how the
We propose a
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behavior trees and the neuroscientific principle of active inference can be used for
active inference and behavior trees (BTs) for reactive
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for reactive robot behaviors
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complex
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minimisation problem, and how state estimation and symbolic decision making are handled within the same framework. The general
minimization problem, bringing the neuroscientific theory of active inference on a mobile manipulator. In this framework, the general nominal
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behavior trees, where the leaf nodes represent desired states, not actions
BTs. A new type of leaf node, the
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prior
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node, is introduced to specify the desired state to be achieved rather than an action to be executed
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behavior trees
BTs
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a state is left to the online active inference routine, in order to resolve unexpected contingencies. This
the desired state is performed online through active inference. The resulting
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specified through behavior trees, while allowing to cope with the curse of dimensionality in active inference
against unexpected contingencies while considerably reducing the number of nodes in a BT
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the proposed algorithmare analysed in terms of robustness and convergence , and the
our algorithm, such as the convergence and robustness, are thoroughly analyzed and outperform classical BT solutions. The