EAGER: Reconciling Model Discrepancies in Human-Robot Teams

机器人在许多领域极大地补充了人类的能力，但让它们发挥队友的作用往往是具有挑战性的。这在一定程度上是由于对机器人缺乏了解，这往往导致人类发现他们对机器人队友的期望在现实中没有得到满足。 拟议的研究通过让机器人对人们拥有的模型进行推理来解决这个问题，并使用这些模型以更接近预期的方式行事，或者解释感知到的差异。 这项工作可以对涉及人工智能系统的关键领域产生重大影响，例如决策支持，自动化制造，老年人护理和医疗机器人。为了成为可靠的队友，机器人必须了解人类伙伴对机器人任务和能力的期望，并能够在期望模型和实际模型之间寻求协调。在这个项目中，协调将通过以下方式实现：1）偏置机器人的行为以隐式地适应模型差异;或2）通信以显式地减少差异。第一种方法，称为模型协调规划，将被制定为一个优化问题，生成一个计划的机器人执行，同时最大限度地减少其距离的预期计划，人类设想。将开发启发式搜索方法以适应这种方法。第二种方法将生成解释以更新机器人的人类模型，使得机器人的计划更接近于更新模型中人类的期望。 此外，在没有明确提供的情况下，将使用机器学习来近似人类期望的模型，并将开发与这些学习和不完整模型进行模型协调的方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Robots greatly complement human capabilities in many domains, but having them function as teammates has often proven to be challenging. This is due in part to a lack of understanding of robots, which often leads humans to find that their expectations of robotic teammates are not met in reality. The proposed research addresses this by having robots reason about the models people have of them and using those models to either behave in ways that meet the expectations more closely or explain the perceived differences. The work can have a significant impact on critical domains that involve human-in-the-loop AI systems, such as decision support, automated manufacturing, eldercare, and medical robotics. To become reliable teammates, robots must understand the expectations of their human partners regarding the robots' tasks and abilities, and be able to seek reconciliation between the expected and actual models. In this project, reconciliation will be achieved either by 1) biasing the robot's behavior to implicitly accommodate model differences; or 2) communicating to explicitly reduce the differences. The first approach, termed model reconciliation planning, will be formulated as an optimization problem that generates a plan for the robot to execute while minimizing its distance to the expected plan that the human envisions. Heuristic search methods will be developed to accommodate this approach. The second approach will generate explanations to update the human's model of the robot in such a way that the robot's plan more closely matches that of the human's expectation in the updated model. In addition, machine learning will be used to approximate the model of human expectation when not provided explicitly, and methods for model reconciliation with these learned and incomplete models will be developed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.