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.

从自动驾驶汽车到辅助设备到协作工业武器，再到家庭中的个人机器人，相互作用，协作和支持人的机器人是不可避免的。与人的互动应自然，流利且协调良好。 该项目的目的是使用人类行为模型以及机器人的行为如何影响人们对机器人的行为和看法，从而从手工设计的互动策略转变为以可推广的方式产生此类策略的算法。 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做，他们有多信任机器人。该项目将通过开发计划算法来解决这一问题，该算法结合了无法直接观察到的人类代理的内部状态。该项目不是将人类视为需要避免的物理（动态）障碍物，而是提出了一种游戏理论的互动表述，并将其作为一个不足的动态系统介绍了近似值：机器人直接控制其行为，但其行为会影响人类的行为，因此机器人对人的行为有间接影响人类的行为。初步结果导致驾驶领域表明，这提高了机器人效率和与人协调的流畅性。

项目成果

Analyzing Human Models that Adapt Online

• DOI: 10.1109/icra48506.2021.9561652
• 发表时间: 2021-01-01
• 期刊: 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)
• 作者: Bajcsy, Andrea;Siththaranjan, Anand;Dragan, Anca D.
• 通讯作者: Dragan, Anca D.