NRI: FND: Contact-aware Control of Dynamic Manipulation

智能机器人独立工作，并与人类和其他设备协调工作，有可能对社会产生深远的积极影响。为了实现这一目标，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）研究项目将开发用于动态抓取和操纵的多用途算法，以实现广泛应用的人类速度和有效性。例如，对于家用辅助机器人来说，在操纵物体或人时，安全和速度都很重要。 假肢设备也将受益于可靠和动态的半自主性：控制负担往往放在用户身上，用户通过假肢接口的感知和驱动有限。此外，需要与多个工具和部件进行交互的小型和先进制造业可能会从这项研究中受益。该研究的结果也可能有利于机器人在安全关键应用中的使用，例如在救灾工作中。灵巧操作的一个根本挑战在于机器人与物体之间的接触的复杂性。摩擦接触对运动的控制方程提出了数学挑战，特别是不连续性，这导致了混合模式的组合数量。这种组合的复杂性挫败了标准的算法方法，通常将操纵方案限制为先验定义的严格的接触排序。这项研究有两个核心假设。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）控制方法，将是动态的和鲁棒的非结构化操作任务。这种简单性类似于人类对手指力量的控制。其次，通过明确考虑操纵的动态，这项研究将导致比纯粹的静态或准静态方法更强大和更有能力的方法。该算法将在一个物理的、多连杆的机械臂和夹持器上以及在一个模拟的环境中实施和测试。该奖项反映了NSF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.