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.

中文翻译：

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通过直接提问进行模型启发

未来将充满人类在复杂的环境中一起工作的机器人场景。队友互动，机器人的互动必须是获取有关人类（队友）模型的有用信息。在机器人进行交互之前，存在许多挑战，例如将结构差异纳入人体模型中，确保更简单的响应等。在本文中，我们研究了机器人如何进行交互以从一组模型中定位人体模型。我们展示了如何产生问题以改善机器人对队友模型的理解。我们在各种计划领域中评估该方法。评估显示，这些问题可以脱机生成，并且可以通过简单的答案帮助完善模型。