Model Elicitation through Direct Questioning
This work addresses the problem of robot-human model alignment for robots working collaboratively with humans in complex environments, focusing on improving robot interaction capabilities.
This paper explores how a robot can interact with a human teammate to identify the human's internal model from a predefined set of models. The authors demonstrate a method for generating questions that allow the robot to refine its understanding of the human's model through simple answers, with questions generatable offline.
The future will be replete with scenarios where humans are robots will be working together in complex environments. Teammates interact, and the robot's interaction has to be about getting useful information about the human's (teammate's) model. There are many challenges before a robot can interact, such as incorporating the structural differences in the human's model, ensuring simpler responses, etc. In this paper, we investigate how a robot can interact to localize the human model from a set of models. We show how to generate questions to refine the robot's understanding of the teammate's model. We evaluate the method in various planning domains. The evaluation shows that these questions can be generated offline, and can help refine the model through simple answers.