Researchers teach robots to use inference to complete complex tasks

There's much robots can achieve by observing human demonstrations, like the actions necessary to move a box of crackers from a counter to storage. But imitation learning is by no means a perfect science -- demonstrators often complete subgoals that distract systems from overarching tasks. To solve this, researchers at the University of Washington, Stanford University, the University of Illinois Urbana-Champaign, the University of Toronto, and Nvidia propose an "inverse planning" system that taps motions or low-level trajectories to capture the intention of actions. After evaluating their technique by collecting and testing against a corpus of video demonstrations conditioned on a set of kitchen goals, the team reports that their motion reasoning approach improves task success by over 20%. The researchers lay out the full extent of the problem in a preprint paper detailing their work.