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.

该赠款支持将在机器人操纵和积极看法的交集中贡献新知识的研究，从而促进科学进步和国家繁荣的发展。为了启用可以与世界提供复杂的物理互动的通用机器人，在有限的感知下机器人起作用的能力至关重要。但是，随着传统方法依次提出感知和操纵为脱钩的系统组件，机器人操纵技能已被感知系统被动限制。该奖项支持研究建立一个新的范式，以实现感知和操纵之间的互动，并从根本上改变双方的作用以互相支持。关键概念被称为自我认同，是一个过程，机器人在没有完全了解系统甚至本身的情况下开始操纵对象，而同时为感知组成部分创造机会，以获取不可能的必要信息，而这些信息是不可能的。反过来，通过获得的额外信息，可以显着升级操作能力。由于这种新的能力可以改善许多现实的机器人应用，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使美国经济和社会受益。这项研究涉及从计算机科学，机械工程和传感器技术到控制理论和人工智能的几个主题。多学科的框架将扩大代表性不足的群体的参与并积极影响工程教育。该项目利用机器人操纵器中的各种被动（或低级别）适应性的能力，尤其是来自机械稳定性（弹簧或软结构），允许驾驶机构的机械性机制，以实现的机械性能，这些机构允许机器人进行探索，以实施机器人的行为一种自适应估计方案来自我识别系统。机器人对象环境系统将在维持所需状态和稳定性的同时以基本开环的方式进行重新配置，而对这些更改的外部观察则用于生成在线估算和控制器。从本质上讲，它将传统范式从“感官，计划，行为”更改为“行动，感官，计划”，重点是在有限的敏感性下执行高效有效的任务。研究团队将建立通用的自我识别框架，以用于模型完整，模型完整和无模型的操纵系统：1）在需要的情况下弥补有限的感知能力，以完成传统上不可行的任务； 2）最大化其感知能力，以进一步提高系统的任务意识和鲁棒性； 3）将自我识别的结果或整个过程纳入操纵计划和控制中，以在身体和敏感性限制下进行牢固的操作。该项目得到了机器人计划中的跨领域基础研究的支持，共同管理和资助了由工程局（ENG）和计算机和信息科学和工程（CISE）的授权（CISE）。基金会的智力优点和更广泛的影响评论标准。