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

职业：走向自主生成机器人行为以与人类协调——考虑对人类行为的影响

• 批准号: 1652083
• 负责人: Anca Dragan
• 金额: $ 46.18万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652083&HistoricalAwards=false
• 关键词: CAREER; Towards; Autonomously; Generating; Robot

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.

从自动汽车到辅助设备，到协作工业手臂，再到家庭中的个人机器人，机器人的互动、协作和支持是不可避免的。与人的互动应该是自然的，流畅的，协调良好的。 该项目的目的是从手工设计的互动策略，以一种可推广的方式产生这样的战略算法，使用人类行为的模型，以及如何机器人？的行为可能会影响人？机器人的动作和感知。 该项目还涉及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本的观点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它，与人互动时的一个重要标准是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。该项目将通过开发规划算法来解决这一问题，该算法将人类代理的内部状态结合起来，而这些状态是不可直接观察的。而不是把人类作为一个物理（动态）的障碍，需要避免的，这个项目提出了一个互动的博弈论公式，并介绍了一个近似它作为一个欠驱动的动态系统：机器人有直接控制其行动，但它的行动影响人类的行动，所以机器人间接影响人类做什么。在驾驶领域的初步结果表明，这提高了机器人的效率和流畅的协调与人。