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

RR：协作研究资源：学习人手来控制灵巧的机器人手

• 批准号: 0423546
• 负责人: Nancy Pollard
• 金额: $ 20.62万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0423546&HistoricalAwards=false
• 关键词: RR; Collaborative; Research; Resources; Learning

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.

本项目研究人类抓取和操作策略，以深入了解人类受试者在抓取和操作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来创建更好的机器人手部控制算法。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，以及使人工控制的算法（如远程操作和假肢装置的控制）对精细操作任务更加直观和有效的技术。虽然高自由度（DOF）设备可能需要操纵各种各样的对象和执行各种各样的任务，但在任何给定的任务情况下，只有少量的独立控制的DOF可能是必要的。为了在灵巧抓取和操纵方面取得重大进展，我们必须：尽可能充分利用现有的传感技术（特别是力传感），并了解如何在自由度降低的空间中分析、计划和控制手部运动（利用基于任务的协调规则和协同作用，可能使实时抓取优化和规划易于处理）。基础设施应该有助于回答以下问题：什么是最佳抓取，以及它如何依赖于进行抓取的设备的运动学和动力学特性？在任务执行过程中，关键信号、肌肉激活水平、手部形状和力量产生之间的关系是什么？对从皮质信号到强制生产的管道收集的数据进行分析，是否允许轻松地组织成掌握原语，或者产生一个机器人手的人类操作员会觉得直观的“控制手柄”？人类对抓取和操作任务的演示如何被用作它们似乎是美妙的资源，也就是说，如何将单个示例转换为控制算法，使其在机器人上起作用，并且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类手部运动和力量产生的理解，在假肢装置的远程操作和控制中使用力量信息，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，收集的数据将在网络上提供。