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

该学院早期职业发展(CALEAR)奖支持在非结构化环境中进行通用机器人操作的研究。大多数现实世界的操作任务涉及不确定性、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已经达到了硬限制。该奖项支持一项研究，该研究试图建立一种新的范式，使机器人能够通过“操纵漏斗”的镜头来处理不确定性和未知。操作漏斗的概念与普通使用的漏斗相同，其中的思想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到较小的集合，以确保后续机器人的动作对不确定性具有鲁棒性。这一新的范例将改善现实世界中的机器人应用，例如用于工业生产、家庭服务和医疗保健的应用。该奖项还将支持几个STEM倡议，重点是扩大对代表不足的群体的参与，包括动手操作机器人操作教程和随附的一本书，课程增强与研究成果，以及本科生和K-12学生的研究机会。该项目的目标是通过推广任务空间中几何操作漏斗的概念到新的漏斗类别，以针对环境不确定因素进行健壮和灵活的操作，从而偏离传统的感知、规划和控制管道。在这个上下文中，重点是识别条目、塑造颈部，并在这些新的操作漏斗类中找到出口。例如，通过利用主动或被动合规，最初被阻挡的漏斗可以被主动打开，通过自我稳定的任务编队来精确操纵对象，并通过扩大规划空间和简化控制来促进接触丰富的操纵。类似地，通过利用运动和任务约束，可以主动地创建漏斗，以及时将状态转移限制在笼子中，从而有效地减少不确定性，甚至直接找出从不确定的操作输入到其可能的输出的映射。此外，通过通过任务传递漏斗和通过漏斗级联组成多模式操纵解决方案，所提出的基于漏斗的框架将在确保健壮性的同时实现复杂的操纵任务。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情况下工作。该项目由跨部门机器人基础研究计划支持，该计划由工程总监(ENG)和计算机和信息科学与工程(CEISE)共同管理和资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.