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.

该学院早期职业发展（CAREER）奖支持非结构化环境中通用机器人操作的研究。大多数现实世界的操纵任务都涉及不确定性、不可建模的物理场和未知的参数，传统的精确规划和控制方法已经遇到了硬性限制。该奖项支持旨在建立一种新颖范式的研究，使机器人能够通过“操纵漏斗”的镜头处理不确定性和未知因素。操纵漏斗的概念与普通使用漏斗的概念相同，其中的想法是通过由机器人顺应性或运动策略定义的限制性颈部将大量任务可能性过滤为较小的集合，以确保后续机器人动作对不确定性具有鲁棒性。这种新范例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应用。该奖项还将支持多项 STEM 计划，重点是扩大代表性不足群体的参与，包括机器人操作实践教程和随附书籍、研究成果的课程强化以及本科生和 K-12 学生的研究机会。该项目的目标是通过将几何操作漏斗的思想推广到机器人操作中，摆脱传统的机器人操作感知、规划和控制流程。 基于机器人顺应性和运动策略的新型漏斗的任务空间，可针对环境不确定性进行稳健而灵巧的操作。在此背景下，重点是识别这些新类型的操纵漏斗中的入口、塑造瓶颈并找到出口。例如，通过利用主动或被动的顺从性，最初被堵塞的漏斗可以主动“打开”，通过自稳定任务形态精确地操纵物体，并通过扩大的规划空间和简化的控制来促进丰富的接触操纵。类似地，通过利用运动和任务约束，可以主动创建漏斗来及时捕获状态转换，以有效减少不确定性，甚至直接找出从不确定的操作输入到可能输出的映射。此外，通过任务转移漏斗并通过漏斗串联组成多模式操作解决方案，所提出的基于漏斗的框架将能够实现复杂的操作任务，同时坚定地保证鲁棒性。因此，该项目将使机器人能够通过非传统但更可靠的框架进行操作，使它们能够在传统上不可行的高度不确定的场景中工作。该项目得到了机器人学跨部门基础研究项目的支持，该项目由工程理事会（ENG）和计算机与信息科学与工程理事会（CISE）共同管理和资助。该奖项反映了 NSF 的法定使命，并已被 通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。