RR:Collaborative Research Resources: Learning from Human Hands to Control Dexterous Robot Hands

该项目研究人类抓取和操纵策略，以深入了解人类主体在抓取和操纵任务中所采用的协同作用，旨在探索如何最好地利用这些发现为机器人手创建更好的控制算法。特别重要的是使自主机器人行为对不确定性具有鲁棒性的技术，以及使人类参与回路的算法，例如远程操作和假肢设备的控制，对于精细操作任务更加直观和有效。尽管可能需要高自由度（DOF）设备来操纵各种各样的物体并执行各种各样的任务，但是在任何给定的任务情况下，可能仅需要少量的独立控制的DOF。为了在灵巧抓取和操作方面取得重大进展，我们必须：尽可能充分利用现有的传感技术（特别是力传感），并了解如何分析，规划，并且在减小的自由度空间中控制手部运动（利用基于任务的协调规则和协同作用，可以使真实的时间把握优化和规划易于处理）基础设施应有助于回答以下问题：什么是最佳抓取，以及它如何取决于进行抓取的设备的运动学和动力学特性？在执行任务时，关键信号、肌肉激活水平、手形和力量产生之间的关系是什么？对从皮层信号到力产生的沿着管道收集的数据进行分析，是否允许容易地组织成抓握原语，或者导致机器人手的人类操作员会发现直观的“控制手柄”？如何才能将人类抓握和操纵任务的演示作为他们似乎是的奇妙资源，即，如何将单个实例转换为控制算法，使其在机器人上发挥作用，并对变化和不确定性具有鲁棒性？更广泛的影响：研究结果将提高对人手运动和力产生的理解，力信息在假肢设备的远程操作和控制中的使用，以及通过任务演示指导机器人行为的能力。本科生团队将使用该设施，收集的数据将在网上提供。

This project, investigating human grasping and manipulation strategies to gain insight into synergies employed by human subjects in grasping and manipulation tasks, aims to explore how best to use these findings to create better control algorithms for robot hands. Of particular importance are techniques to make autonomous robot behavior robust to uncertainties and to make algorithms with a human in the loop, such as teleoperation and control of prosthetic devices, more intuitive and effective for fine manipulation tasks. Although a high degree of freedom (DOF) device may be required to manipulate a wide variety of objects and perform a wide variety of tasks, in any given task situation, only a small number of independently controlled DOF may be necessary. To make significant progress towards dexterous grasping and manipulation, we must: Make the best possible use of available sensing technology (specially for force sensing), and Understand how to analyze, plan, and control hand motion in a reduced degree of freedom space (take advantage of task-based coordination rules and synergies that may make real time grasp optimization and planning tractable).The infrastructure should contribute in answering the following questions: What is an optimal grasp, and how does it depend on the kinematic and dynamic properties of the device doing the grasping? What is the relationship between critical signals, muscle activation levels, hand shape, and force production during task performance? Does analysis of data collected along the pipeline from cortical signals to force production allow easy organization into grasp primitives or result in "control handles" that a human operator of a robot hand would find intuitive? How can human demonstrations of grasping and manipulation tasks be employed as the wonderful resource they seem to be, i.e., how can individual examples be converted into control algorithms that will function on a robot and be robust to variations and uncertainties?Broader Impact: The results will improve the understanding of human hand motion and force production, the use of force information in teleoperation and control of prosthetic devices, and the ability to coach robot behavior through task demonstration. Teams of undergraduate students will use the facility and data collected will be made available on the web.