Asking for Help Effectively via Modeling of Human Beliefs

Autonomous robots deployed around humans must be able to ask for help when problems arise. However, people may have incorrect mental models of the robots» capabilities or task, making them unable to help. We propose a data-driven method to estimate humans» beliefs after hearing task-related utterances and build sets of utterances that influence people towards useful help in expectation. We present an example to show our method selects effective utterances when the desired help is much different than a person expects.