Mental-state inference to support human-robot collaboration

In this video, the Leonardo robot infers mental states from the observable behavior of two human collaborators in order to assist them in achieving their respective goals. The robot engages in a simulation-theory-inspired approach to make these inferences and to plan the appropriate actions to achieve the task goals. Each person wants a different food item (chips or cookies), locked in one of two larger boxes. The robot can operate a remote control interface to open two smaller boxes, one containing chips and the other cookies. The task is inspired by the Sally-Anne false-belief task, where the humans have diverging beliefs caused by a manipulation witnessed by only one of the participants. The robot must keep track of its own beliefs, in addition to inferring the beliefs of the human collaborators, as well as infer their respective goals, to offer the correct assistance.