Putting away the Groceries with Precise Semantic Placements

We present a methodology for a service robot to put away the groceries in a precise and stable configuration that satisfies the desired semantic relationship of grocery and pantry objects. This task is invaluable to the motor impaired and increases the capability of contextualized service robots. We show that augmenting traditional geometric assessment with contextual knowledge of food categorization and a language association model, allows the robot to complete this task efficiently. We quantitatively validate our approach with a data set of 51 common grocery items, of which 11 objects are used for real world experiments. According to our evaluation, our method is able to successfully shelve objects next to semantically related objects 100% of the time when these relationships exist. We achieve this with an average placement precision of 3.2cm and a standard deviation of 1.1cm. We discuss remaining challenges and needed improvements for robots with these capabilities to be introduced to home environments.