NRI: FND: Contact-aware Control of Dynamic Manipulation

智能机器人可以独立工作，也可以与人类和其他设备协调工作，对社会产生深远的积极影响。为了实现这一承诺，机器人必须在复杂、高度不确定的环境中快速、有能力和安全地进行交互。在这种情况下，抓取和灵巧操作的基本任务至关重要：当机器人接触周围环境时，它们通常会缓慢而谨慎地操作，以避免任何意外接触或损坏。这个国家机器人计划（NRI）的研究项目将开发用于动态抓取和操纵的多用途算法，以在广泛的应用中实现类似人类的速度和效率。例如，对于家用辅助机器人来说，在操作物体或人时，安全性和速度都很重要。假肢设备也将受益于可靠和动态的半自治：通常控制负担放在用户身上，用户通过假肢接口的传感和驱动有限。此外，需要与多种工具和零件相互作用的小型和先进制造业都可能从这项研究中受益。这项研究的结果也可能有利于机器人在安全关键应用中的应用，比如救灾工作。灵巧操作的一个基本挑战是机器人与物体接触的复杂性。摩擦接触对运动控制方程提出了数学上的挑战，特别是导致混合模式组合数量的不连续。这种组合的复杂性使标准算法方法受挫，通常将操作方案限制为严格的接触顺序，定义为先验。这项研究有两个中心假设。首先，正式的、基于优化的数值方法可以发现和验证简单的（非组合的）控制方法，这些方法对于非结构化操作任务来说既动态又鲁棒。这种简单性与人类对手指力量的控制相似。其次，通过明确考虑操纵的动态，本研究将导致比纯静态或准静态方法更健壮和更有能力的方法。这些算法将在一个物理的、多连杆的手臂和抓手以及模拟环境中实施和测试。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Working independently and in coordination with humans and other devices, intelligent robots have the potential for profound positive impact on society. To achieve this promise, robots must be fast, capable, and safe as they interact in complex, highly uncertain environments. In this context, the fundamental tasks of grasping and dexterous manipulation are critical: when robots touch their surroundings, they typically operate slowly and cautiously to avoid any accidental contact or damage. This National Robotics Initiative (NRI) research project will develop multi-purpose algorithms for dynamic grasping and manipulation to achieve human-like speed and effectiveness for a broad range of applications. For in-home assistive robots, for example, safety and speed are both important when manipulating either objects or people. Prosthetic devices would also benefit from reliable and dynamic semi-autonomy: too often the control burden is placed on the user, who has limited sensing and actuation through the prosthetic interface. Additionally, both small and advanced manufacturing industries that require interaction with multiple tools and parts may benefit from this research. Outcomes of this research may also benefit robotic use in safety-critical applications, such as in disaster relief efforts.A fundamental challenge in dexterous manipulation lies in the complexity of the contact between robot and object. Frictional contact introduces mathematical challenges to the governing equations of motion, particularly discontinuities which result in a combinatorial number of hybrid modes. This combinatorial complexity frustrates standard algorithmic methods, typically limiting manipulation schemes to a strict sequencing of contacts, defined a priori. This research has two central hypotheses. First, that formal, optimization-based numerical methods can discover and verify simple (non-combinatoric) control approaches that will be both dynamic and robust for unstructured manipulation tasks. This simplicity parallels human control of finger forces. Second, by explicitly considering the dynamics of manipulation, this research will lead to more robust and capable approaches than purely static or quasi-static methods. The algorithms will be implemented and tested on a physical, multi-link arm and gripper and in a simulated environment.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.