CAREER: Exploring Robust Robot Manipulation through Compliance- and Motion-based Manipulation Funnels

职业：通过基于顺应性和运动的操纵漏斗探索鲁棒的机器人操纵

• 批准号: 2240040
• 负责人: Kaiyu Hang
• 金额: $ 60万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240040&HistoricalAwards=false
• 关键词: CAREER; Exploring; Robust; Robot; Manipulation

This Faculty Early Career Development (CAREER) award supports research in general-purpose robotic manipulation in unstructured environments. Most real-world manipulation tasks involve uncertainties, un-modellable physics, and unknown parameters, where traditional approaches for precise planning and control have been hitting a hard limit. This award supports research that seeks to establish a novel paradigm that enables robots to handle uncertainties and unknowns through the lens of “manipulation funnels.” The concept of manipulation funnel is the same as that of an ordinary use funnel, wherein the idea is to filter a large set of task possibilities through a restrictive neck, defined by robot compliance or motion strategy, to a smaller set ensuring that the subsequent robot actions are robust against uncertainties. This new paradigm will improve real-world robot applications, such as those used in industrial production, household services, and healthcare. The award will also support several STEM initiatives, with focus on broadening participation to underrepresented groups, including hands-on robotic manipulation tutorials and an accompanying book, curriculum enhancement with research outcomes, and research opportunities for undergraduate and K-12 students.The objective of this project is to depart from the traditional pipeline of perception, planning, and control for robotic manipulation by generalizing the idea of geometric manipulation funnels in task space to new classes of funnels based on robot compliance and motion strategy for robust and dexterous manipulation against environmental uncertainties. Within this context, the focus is on identifying the entries, shaping the necks, and finding the exits in these new classes of manipulation funnels. For example, by leveraging active or passive compliance, funnels that are initially blocked can be can actively “opened” to precisely manipulate objects through self-stabilizing task formations and facilitate contact-rich manipulation with enlarged planning spaces and simplified control. Similarly, by leveraging motions and task constraints, funnels can be actively created to cage the state transitions in time to effectively reduce uncertainties or even directly figure out the mapping from uncertain manipulation inputs to their possible outputs. Furthermore, by transferring funnels through tasks and composing multi-modal manipulation solutions via funnel concatenations, the proposed funnel-based framework will enable complex manipulation tasks while firmly guaranteeing robustness. As a result, this project will enable robots to manipulate through a non-traditional but more reliable framework, allowing them to work in highly uncertain scenarios that were traditionally infeasible.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.

该教师早期职业发展（职业）奖支持在非结构化环境中进行通用机器人操纵的研究。大多数真实的操纵任务都涉及不确定性，不可验证的物理学和未知参数，在这些参数中，精确计划和控制的传统方法已受到严格限制。该奖项支持旨在建立一种新颖的范式的研究，该范式使机器人能够通过“操纵漏斗”的镜头处理不确定性和未知数。操纵漏斗的概念与普通使用漏斗的概念相同，其中，该想法是通过限制性颈部（由机器人合规性或运动策略定义的限制性颈部）过滤到较小的设置，以确保随后的机器人动作可靠，以防止不确定性。这种新的范式将改善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中使用的机器人。该奖项还将支持多项STEM举措，重点是扩大人为不足的群体的参与，包括动手的机器人操纵教程以及一本参与的书籍，通过研究成果来增强课程，以及本科生和K-12学生的研究机会。基于机器人合规性和运动策略的新类别渠道的空间，以对环境不确定性进行稳健和灵活的操纵。在这种情况下，重点是识别条目，塑造脖子，并在这些新的操纵漏斗中找到出口。例如，通过利用主动或被动合规性，可以主动“打开”最初被阻止的漏斗通过自我稳定的任务形式来精确操纵对象，并通过增加的计划空间和简化的控制来促进接触式操纵。同样，通过利用动作和任务约束，可以积极创建funnels及时封闭状态过渡以有效地减少不确定性，甚至直接从不确定的操纵输入到其可能的输出中直接找出映射。此外，通过通过任务转移funnels并通过漏斗串联组成多模式操纵解决方案，拟议的基于漏斗的框架将实现复杂的操纵任务，同时确保稳健性。结果，该项目将使机器人能够通过非传统但更可靠的框架来操纵机器人，从而使它们能够在传统上不可行的高度不确定的情况下工作。该项目得到了机器人技术计划的跨指导基础研究的支持，该项目由工程局（ENG）以及计算机和信息科学与工程局（CISE）共同管理和资助。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛影响的评估来审查Criteria通过评估来通过评估来获得支持的。