CAREER: Towards Autonomously Generating Robot Behavior for Coordination with Humans -- Accounting for Effects on Human Actions

从自动驾驶汽车，到辅助设备，到协作的工业手臂，再到家庭中的个人机器人，与人互动、协作并为人类提供支持的机器人是不可避免的。与人的互动应该是自然、流畅和协调的。该项目旨在利用人类行为的模型，以及机器人S的动作可能如何影响人们的S动作和对机器人的感知，从手工设计的交互策略转向以一种普遍的方式产生此类策略的算法。该项目还致力于增强这一愿景的教育目标：使下一代学生能够通过计算和以人为中心的观点相结合来定义和解决机器人和人工智能问题。虽然机器人算法通常会推理世界的物理状态以及机器人如何以有用的方式影响世界，但与人互动时，一个重要的标准是这些行为如何影响他们和他们的内部状态：他们计划做什么，他们认为机器人会做什么，他们对机器人的信任程度。该项目将通过开发计划算法来解决这个问题，该算法结合了不可直接观察到的人类代理的内部状态。该项目没有将人类视为需要避免的物理(动态)障碍，而是提出了一种互动的博弈论公式，并引入了一种近似于驱动不足的动力系统：机器人直接控制其行动，但其行动影响人类的行动，因此机器人间接影响人类的行为。驾驶领域的初步结果表明，这提高了机器人的效率和与人协调的流畅性。

Robots that interact, collaborate, and come in support of people are inevitable, from autonomous cars, to assistive devices, to collaborative industrial arms, to personal robots in the home. Interactions with people should be natural, fluent, and well-coordinated. The project aims to move from hand-designed strategies for interaction to algorithms that produce such strategies in a generalizable way, using models of human behavior and how a robot?s actions may affect people?s actions and perceptions of the robot. The project also addresses educational goals that augment this vision: enabling the next generations of students to define and solve robotics and AI problems through a combination of computational and human-centered perspectives.While robotics algorithms typically reason about the physical state of the world and how the robot can affect it in useful ways, an important criterion when interacting with people is how the actions affect them and their internal state: what they plan to do, what they think the robot will do, how much they trust the robot. The project will address this by developing planning algorithms that incorporate the internal state of the human agent that is not directly observable. Rather than treating the human as a physical (dynamic) obstacle that needs to be avoided, this project proposes a game-theoretic formulation of interaction, and introduces an approximation to it as an underactuated dynamical system: the robot has direct control over its actions, but its actions affect the human's actions, and so the robot indirectly influences what the human does. Preliminary results in a driving domain suggest that this improves robot efficiency and fluency of coordination with people.