Learning user preferences for robot-human handovers

The ability to hand objects to users is a key skill for service robots. While the main purpose of a handover action is to successfully transfer an object to the user, it is also relevant that the robot takes the user's preferences into consideration when deciding which handover strategy to use. In a recent online study, we found evidence that suggests two important facts: (1) Users find value in a robot capable of performing handover tasks in more than one manner, and (2) Different users display different preferences on how they would like to be handed a requested object. Therefore, in this work we are proposing a novel system for learning handover preferences. Our system is evaluated in both simulation and on a real robot using computational perception to estimate human activity and environment type.