Learning spatial relations from functional simulation

Robots acting in complex environments need not only be aware of objects, but also of the relationships objects have with each other. This paper suggests a conceptualization of these relationships in terms of task-relevant functional distinctions, such as support, location control, protection and confinement. Being able to discern such relations in a scene will be important for robots in practical tasks; accordingly, it is demonstrated how predictive models can be trained using data from physics simulations. The resulting models are shown to be both highly predictive and intuitively reasonable.