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（职业）奖支持非结构化环境中通用机器人操纵的研究，大多数现实世界的操纵任务都涉及不确定性、不可建模的物理和未知参数，而传统的精确规划和控制方法一直在这些任务中遇到困难。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”的视角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。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一组，确保后续的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，例如所使用的机器人应用。该奖项还将支持多项 STEM 举措，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、包含研究成果的课程强化以及研究机会。本科生和 K-12 学生。目标该项目的目的是通过将任务空间中的几何操作漏斗的思想推广到基于机器人顺应性和运动策略的新型漏斗，以实现鲁棒而灵巧的操作，从而脱离机器人操作的传统感知、规划和控制流程在这种背景下，重点是识别这些新型操纵漏斗的入口、塑造瓶颈并找到出口。例如，通过利用主动或被动的合规性，可以改变最初被堵塞的漏斗。主动“打开”精准操控类似地，通过利用运动和任务约束，可以主动创建漏斗以及时控制状态转换，从而有效减少不确定性甚至直接计算。此外，通过任务转移漏斗并通过漏斗串联组成多模式操纵解决方案，所提出的基于漏斗的框架将实现复杂的操纵任务，同时坚定地保证鲁棒性。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。该项目得到了跨部门机器人基础研究计划的支持，共同管理和该奖项由工程理事会 (ENG) 和计算机与信息科学与工程理事会 (CISE) 资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。