NRI: Mutually Assistive Robotics

NRI：互助机器人

• 批准号: 2132887
• 负责人: Elaine Short
• 金额: $ 149.95万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132887&HistoricalAwards=false
• 关键词: NRI; Mutually; Assistive; Robotics

This project will advance the state-of-the-art for how robots that render assistance to users with disabilities interact with and learn from those users. Most current work takes a deficit-based approach to disability, in which the robot is assumed to be the more competent partner in the interaction and the user with disabilities provides high-level goals to a system that primarily helps them with mundane tasks of daily living. This approach is deficient in two significant ways: first, it removes agency and control from users, directly counteracting the psychological benefits of assistive technology and potentially replacing a loss of independence to caregivers with a loss of independence to robots; and second, it fails to address tasks that improve quality of life, such as artistic expression or grooming, where the specific sequence of actions, the manner in which those actions are carried out, and control over those actions is the goal itself. This research takes a strengths-based approach to assistive robotics, developing new methods that allow the robot and user to freely assist each other to complete tasks, and evaluating those methods in activities that improve people's quality of life and where users' autonomy and control over both the goal and manner of completing a task are important. Project outcomes will include new methods for robot learning that empower people with disabilities to collaboratively design, control, and influence robot behavior while engaging in pleasurable hobbies, controlling their own appearance, and generally engaging in creative interaction with the world. These methods will help to ensure that the next generation of assistive robotics support the quality of life and joyous self-expression for people with disabilities, as well as their daily chores This should significantly improve the lives of the substantial number of Americans of all ages who live with physical disabilities. Additional broad impact will derive from algorithms for human-robot interaction that significantly advance not only that field but also inform future work in interactive reinforcement learning, learning for robotics, and intelligent assistive technologies.Leveraging the team's expertise in assistive technology, human-robot interaction, augmented reality, and human-robot interaction, project goals will be achieved through three technological innovations. First, algorithms to enhance initial model learning with mutual assistance from robot to human and human to robot at multiple levels of abstraction, from direct control to language. Second, new methods for giving users usable mental models of the robot, such as selecting and displaying information through augmented reality to empower users to understand robot perception and decision-making and improve their ability to influence robot behavior. Finally, new interactive learning algorithms that enable users to exploit feedback after initial learning and ensure that users can influence the manner in which tasks are conducted as well as task goals. These algorithms will be united in a three-layer architecture for assistive robotics that explicitly supports assistance from both robot to human and human to robot at each level. At the lowest level is a data-driven mapping from sensory state to movement. At the middle level, those motions are named as atomic actions such as reaching, pouring, or grasping, and grouped based on parameters such as target objects or features of the motion. At the highest level, actions are represented symbolically with pre- and post-conditions and combined into multi-step plans that achieve user-specified goals while being modified online by the user. In addition to supporting both robot-to-human and human-to-robot assistance at all levels, this architecture will also allow for the flow of information between levels, especially in robot-to-human assistance. The work will be validated with the help of expert user-collaborators with disabilities, as well as in larger-scale studies that validate foundational technological developments.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.

该项目将推进为残疾用户提供帮助的机器人如何与这些用户互动并向他们学习的最新技术。 目前的大多数工作都采取了基于缺陷的方法来解决残疾问题，其中机器人被认为是互动中更有能力的合作伙伴，而残疾用户则为系统提供了高层次的目标，主要帮助他们完成日常生活中的平凡任务。 这种方法在两个重要方面存在缺陷：首先，它剥夺了用户的代理和控制权，直接抵消了辅助技术的心理益处，并可能以失去对机器人的独立性来取代失去对照顾者的独立性;第二，它未能解决提高生活质量的任务，如艺术表达或修饰，其中具体的行动顺序，执行这些行动的方式以及对这些行动的控制是目标本身。 这项研究采取了基于优势的方法来辅助机器人，开发新的方法，允许机器人和用户自由地帮助对方完成任务，并评估这些方法在活动中，提高人们的生活质量和用户的自主权和控制的目标和完成任务的方式是很重要的。 项目成果将包括机器人学习的新方法，使残疾人能够协同设计，控制和影响机器人行为，同时从事愉快的爱好，控制自己的外观，并与世界进行创造性的互动。 这些方法将有助于确保下一代辅助机器人支持残疾人的生活质量和快乐的自我表达，以及他们的日常家务，这将大大改善所有年龄段的美国人的生活。 此外，人机交互算法也将产生广泛的影响，这些算法不仅大大推动了该领域的发展，而且还为交互式强化学习、机器人学习和智能辅助技术的未来工作提供了信息。利用团队在辅助技术、人机交互、增强现实和人机交互方面的专业知识，项目目标将通过三项技术创新来实现。第一，通过从机器人到人类和人类到机器人的相互帮助，在从直接控制到语言的多个抽象层次上增强初始模型学习的算法。第二，为用户提供可用的机器人心智模型的新方法，例如通过增强现实选择和显示信息，使用户能够理解机器人的感知和决策，并提高他们影响机器人行为的能力。最后，新的交互式学习算法，使用户能够利用初始学习后的反馈，并确保用户可以影响任务的执行方式以及任务目标。这些算法将统一在一个三层架构的辅助机器人，明确支持援助从机器人到人类和人类到机器人在每个级别。 在最低层是从感觉状态到运动的数据驱动映射。在中间级别，这些运动被命名为原子动作，例如到达、倾倒或抓取，并根据目标对象或运动特征等参数进行分组。 在最高级别上，行动用前置和后置条件象征性地表示，并组合成多步计划，实现用户指定的目标，同时由用户在线修改。 除了支持各级机器人对人类和人类对机器人的协助外，该架构还将允许各级之间的信息流动，特别是在机器人对人类的协助中。 这项工作将在残疾人专家用户合作者的帮助下进行验证，以及在验证基础技术发展的大规模研究中进行验证。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

From “Thumbs Up” to “10 out of 10”: Reconsidering Scalar Feedback in Interactive Reinforcement Learning

• DOI: 10.1109/iros55552.2023.10342458
• 发表时间: 2023-10
• 期刊: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Hang Yu;Reuben M. Aronson;Katherine H. Allen;E. Short

Keeping Humans in the Loop: Teaching via Feedback in Continuous Action Space Environments

• DOI: 10.1109/iros47612.2022.9982282
• 发表时间: 2022-10
• 期刊: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Isaac S. Sheidlower;Allison Moore;Elaine Schaertl Short