Collaborative Research: Self-Identification for Robot Manipulation under Uncertainty Aided by Passive Adaptability

协作研究：被动适应性辅助的不确定性下机器人操纵的自我识别

• 批准号: 2132823
• 负责人: Aaron Dollar
• 金额: $ 39.9万
• 依托单位: Yale 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: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132823&HistoricalAwards=false
• 关键词: Collaborative; Research; Self; Identification; Robot

This grant supports research that will contribute novel knowledge at the intersection of robotic manipulation and active perception, promoting both the progress of science and the advancement of national prosperity. To enable general-purpose robots that can offer sophisticated physical interactions with the world, the capability of robots to work under limited perception is essential. However, as traditional approaches sequentially formulate perception and manipulation into decoupled system components, robot manipulation skills have been passively constrained by the perception system. This award supports research to establish a new paradigm for enabling the interactions between perception and manipulation, and will fundamentally transform the roles of both to actively facilitate each other. The key concept, termed as self-identification, is a process where robots start to manipulate objects without full knowledge of the system, or even of itself, while in the meantime creating opportunities for the perception component to acquire necessary information that were impossible otherwise. In turn, the manipulation capability is significantly upgraded with the extra information obtained. As this new ability can improve many real-world robot applications, such as industrial production, household services, and healthcare applications, the results from this research will benefit the U.S. economy and society. This research involves several topics ranging from computer science, mechanical engineering, and sensor technology, to control theory and artificial intelligence. The multi-disciplinary framework will broaden the participation of underrepresented groups and positively impact the engineering education.This project leverages various types of passive (or low-level) adaptability in robot manipulators, especially that coming from mechanical compliance (springs or soft structures), low-level impedance control, or underactuated mechanisms, to allow the robot to conduct exploratory motions that are externally observed and used within an adaptive estimation scheme to self-identify the system. The robot-object-environment system will be actively reconfigured while maintaining the desired states and stability in an essentially open-loop way, while external observations of these changes are used to generate online estimations and controllers. Essentially, it changes the traditional paradigm from “sense, plan, act” to “act, sense, plan”, with an emphasis on efficient and effective task execution under limited sensing. The research team will establish generic self-identification frameworks for model-complete, model-incomplete, and model-free manipulation systems to: 1) compensate for limited perception abilities, where needed, to accomplish tasks that are traditionally infeasible; 2) maximize their perception capabilities to further improve the system’s task-awareness and robustness; and 3) incorporate the results or the entire process of self-identification into manipulation planning and control to enable robust manipulation under physical and sensing limitations.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

该补助金支持将在机器人操作和主动感知的交叉点上贡献新知识的研究，促进科学进步和国家繁荣的发展。为了使通用机器人能够与世界进行复杂的物理交互，机器人在有限感知下工作的能力至关重要。然而，由于传统的方法顺序地将感知和操作制定为解耦的系统组件，机器人操作技能受到感知系统的被动约束。该奖项支持研究建立一个新的范式，使感知和操纵之间的相互作用，并将从根本上改变两者的作用，积极促进彼此。关键概念，称为自我识别，是一个过程，机器人开始操纵对象，而不完全了解系统，甚至自己，同时创造机会，为感知组件获得必要的信息，否则是不可能的。反过来，操纵能力随着获得的额外信息而显著升级。由于这种新能力可以改善许多现实世界的机器人应用，例如工业生产，家庭服务和医疗保健应用，因此这项研究的结果将有利于美国经济和社会。这项研究涉及多个主题，从计算机科学，机械工程，传感器技术，控制理论和人工智能。多学科框架将扩大代表性不足的群体的参与，并对工程教育产生积极影响。机器人操作器的（或低级）适应性，特别是来自机械顺应性的适应性（弹簧或软结构）、低水平阻抗控制或欠驱动机构，以允许机器人进行探索性运动，该探索性运动在外部被观察并在自适应估计方案内使用以自识别系统。机器人-物体-环境系统将被主动重新配置，同时以基本上开环的方式保持所需的状态和稳定性，而这些变化的外部观察用于生成在线估计和控制器。从本质上讲，它改变了传统的范式，从“感觉，计划，行动”到“行动，感觉，计划”，强调在有限的感觉下高效和有效的任务执行。研究小组将为模型完整、模型不完整和无模型操纵系统建立通用的自识别框架，以：1）在需要时补偿有限的感知能力，以完成传统上不可行的任务; 2）最大化它们的感知能力，以进一步提高系统的任务意识和鲁棒性;以及3）将自我识别的结果或整个过程纳入操作规划和控制中，以实现在物理和传感限制下的鲁棒操作。该项目得到跨部门机器人基础研究计划的支持，该奖项反映了NSF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。