Before revision:  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 .
After revision:  We show how complex 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 behavior trees, where the leaf nodes represent desired states, not actions as in classical behavior trees .