By developing new ways for computers to anticipate people’s actions, Dorsa Sadigh wants to help pave the way for a future in which human and robots do things like share the roads.
In one widely cited paper from 2016, she and her colleagues considered the idealized case of two cars, one driven by a person and another by a computer program. She first had real people drive a car in a video-game-like simulation with several autonomous counterparts that followed preplanned routes. On the basis of people’s behavior in the simulation, she developed a model for how humans drive, which the robot driver then used to devise new strategies for interacting with them. Without ever being explicitly told to do so, it did things like slowly backing up at an intersection, encouraging the “human” to go first. It also developed an attitude, learning how to cut human drivers off or force them to change lanes by swerving toward them.
More recently Sadigh and Dylan Losey, at the time her postdoctoral student, taught robots in a simulated setting how to trick humans in a game that involves negotiating who will do more work in carrying plates to a table. “This robot is capable of bringing two plates, but misleads the human to believe that it can only carry one in order to reduce its overall effort,” they wrote in a paper on the work. Teaching robots to be lazy might not sound particularly worthwhile. But Sadigh and Losey are thinking of future applications in which robots might be called upon to help stroke patients in their recovery, for example. Robots, they say, “need to make intelligent decisions that motivate user participation.”