NRI: Mutually Assistive Robotics

该项目将推动机器人如何与残疾人互动并向他们学习的最新技术。目前的大多数工作都采用了一种基于缺陷的残疾方法，在这种方法中，机器人被认为是互动中更有能力的伙伴，残疾用户向系统提供高级目标，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，它移除了用户的代理和控制，直接抵消了辅助技术的心理好处，并可能以机器人的独立性丧失取代护理人员的独立性丧失；其次，它不能解决提高生活质量的任务，比如艺术表达或修饰，在这些任务中，特定的行动顺序，执行这些行动的方式，以及对这些行动的控制本身就是目标。本研究采用基于优势的方法来研究辅助机器人，开发新的方法，允许机器人和用户自由地相互帮助完成任务，并在提高人们生活质量的活动中评估这些方法，在这些活动中，用户对完成任务的目标和方式的自主和控制是很重要的。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、控制和影响机器人的行为，同时从事愉快的爱好，控制自己的外表，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，以及他们的日常杂务。这将显著改善美国所有年龄的大量身体残疾人士的生活。人机交互算法将产生更广泛的影响，这不仅会显著推进该领域的发展，还会为交互式强化学习、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。首先，在从直接控制到语言的多个抽象层次上，通过从机器人到人、从人到机器人的相互帮助来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，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在初始学习后利用反馈，并确保用户可以影响任务执行的方式以及任务目标。这些算法将统一在辅助机器人的三层架构中，明确支持每个级别的机器人对人和人对机器人的帮助。最低层次是由数据驱动的从感觉状态到运动的映射。在中间层，这些动作被命名为原子动作，如到达、倾倒或抓取，并根据目标对象或动作特征等参数进行分组。在最高级别，操作用前置和后设条件象征性地表示，并组合成多步骤计划，实现用户指定的目标，同时由用户在线修改。除了支持所有级别的机器人对人和人对机器人的协助外，该架构还将允许不同级别之间的信息流动，特别是在机器人对人的协助中。这项工作将在残疾专家用户合作者的帮助下以及在验证基础技术发展的更大规模研究中得到验证。该奖项反映了美国国家科学基金会的法定使命，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。

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.