A self-driving car might learn to maneuver more nimbly among human drivers if it didn’t get lost in the details of their every twist and turn.
Think of all the subconscious processes you perform while you’re driving. As you take in information about the surrounding vehicles, you’re anticipating how they might move and thinking on the fly about how you’d respond to those maneuvers. You may even be thinking about how you might influence the other drivers based on what they think you might do.
If robots are to integrate seamlessly into our world, they’ll have to do the same. Now researchers from Stanford University and Virginia Tech have proposed a new technique to help robots perform this kind of behavioral modeling, which they will present at the annual international Conference on Robot Learning next week. It involves the robot summarizing only the broad strokes of other agents’ motions rather than capturing them in precise detail. This allows it to nimbly predict their future actions and its own responses without getting bogged down by heavy computation.
A different theory of mind
Traditional methods for helping robots work alongside humans take inspiration from an idea in psychology called theory of mind. It suggests that people engage and empathize with one another by developing an understanding of one another’s beliefs—a skill we develop as young children. Researchers who draw upon this theory focus on getting robots to construct a model of their collaborators’ underlying intent as the basis for predicting their actions.