CAREER: From Grasp Quality to Hand Quality: Analysis and Optimization for Effective Robot Hands

机器人操纵器通常具有高精度、可重复性和抓握强度，这些特性使得许多应用成为可能，主要是在工业环境中。然而，机器人手仍然缺乏通用性：一旦操作任务意味着变化，它很快就会超出当今机器人的能力，福尔斯。这阻止了机器人操作器在许多环境中操作，从杂乱的储藏室和仓库到典型的家庭和其他人类环境。该项目旨在开发和展示更多功能的机器人手，能够操纵各种物体并在混乱中操作。配备这种手的机器人可以执行其他容易受伤的任务，如装配和分拣（制造业），或订单分拣和包装（物流）。在医疗保健领域，更通用的机器人操纵器可以协助日常生活活动，其中许多活动涉及接触和操纵。从教育的角度来看，该项目旨在通过结合传统上跨越多个工程学科的机器人领域来鼓励跨学科培训，并通过激励和培养高中和本科阶段的年轻学生从事STEM职业来促进工程教育的多样性。该项目的主要研究目标是找到构建和使用机器人手的新方法，其中高级抓取规划算法、低级控制回路和硬件设计本身在紧密回路中同时优化，并且相互考虑。这代表着与“构建，然后编程”方法的背离，在这种方法中，只有在设计和构建了底层硬件（手）之后，才研究执行复杂任务（如抓取和操纵）的算法。通过将这些阶段结合起来，我们可以共同优化相互适应和互补的算法，传感器阵列，控制策略和机构结构。机械和算法的复杂性，然后可以增加严格的基础上可证明的性能增益。

Robotic manipulators often exhibit high precision, repeatability, and grip strength, characteristics which have enabled many applications, primarily in industrial settings. However, robot hands still lack versatility: once a manipulation task implies variation, it quickly falls beyond the capabilities of today's robots. This prevents robot manipulators from operating in many environments, from cluttered store rooms and warehouses to typical homes and other human settings. This project aims to develop and demonstrate more versatile robot hands, able to manipulate a wide range of objects and to operate in clutter. Robots equipped with such hands could perform what are otherwise injury-prone tasks like kitting and bin picking (in manufacturing), or order picking and packing (in logistics). In healthcare, more versatile robotic manipulators could assist with activities of daily living, many of which involve contact and manipulation. From an educational perspective, this project aims to encourage interdisciplinary training by combining areas of robotics traditionally spanning multiple engineering disciplines, and to promote diversity in engineering education by inspiring and preparing young students for STEM careers, at both the high school and undergraduate levels.The key research objective of this project is to find new ways of building and using robot hands, where the high level grasp planning algorithms, the low-level control loops, and the hardware design itself are optimized simultaneously, in a tight loop, and taking each other into account. This represents a departure from the "build, then program" approach, where algorithms for performing complex tasks (such as grasping and manipulation) are researched only after the underlying hardware (the hand) has been designed and constructed. By combining these stages, we can jointly optimize algorithms, sensor arrays, control strategies and mechanism structures that fit and complement each other. Both mechanical and algorithmic complexity can then be increased based strictly on provable performance gains.