CAREER: Primitives and Policies for Complex Behavior in Human and Robotic Hands

职业：人类和机器人手中复杂行为的原语和策略

• 批准号: 0954254
• 负责人: Veronica Santos
• 金额: $ 55.43万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-15 至 2014-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0954254&HistoricalAwards=false
• 关键词: CAREER; Primitives; Policies; Complex; Behavior

Each human fingertip has approximately 2000 tactile sensors. Stimulation of these sensors triggers reactive grip responses that are mediated by the spine. In comparison to the dexterous capabilities of the human hand, robotic manipulation capabilities in unstructured environments are crude. When controlled by a human operator, robotic manipulators are further limited by restricted information flow (command and sensing) at the human-machine interface. All human-machine systems, from telesurgery robots to neuroprostheses, must address the critical issue of communication delays which can range, depending upon the distance between the human and the machine, from less than a second to hours. For artificial manipulation, even delays of one second can result in adverse events such as increased bleeding from an open incision or increased frustration and eventual disuse of an advanced prosthesis. Taking a cue from biology, autonomous low-level reflexes that detect stimuli and implement a corrective response in robotic hands without a human in the loop could buy time for communication, information processing, and decision-making in human-machine systems. A long-term research objective of the PI is to advance robotic manipulation with grip reflex algorithm primitives, artificial tactile sensors, and generalizable grasp policy algorithms inspired by the human hand. In this project, she will focus on understanding what drives low-level reactive grip responses, how human-machine performance can benefit from the implementation of similar autonomous primitives, and what grasp policies can be learned by a robotic hand. Contributions of this work will include characterization of the reactive grip responses in human hands, development of human-inspired grip reflex algorithm primitives and tactile sensors for robotic hands, and development of learning algorithms that autonomously extract general grasp policies for robotic hands. Research outcomes will enhance our fundamental understanding of grasp primitives in human hands that provide a foundation for dexterous manipulation, and improve the functionality of robotic hands through grip reflex algorithm primitives and learning algorithms that extract grasp policies.Broader Impacts: This research will transform artificial manipulation by enabling robotic grasp with dynamic control of adduction/abduction degrees-of-freedom and use of biomimetic tactile sensors, thereby revolutionizing robotic manipulators intended for unstructured, access-limited, or unsafe environments (including space, underwater, military, rescue, surgery, assistive, rehabilitative, and prosthetic) that require robustness in the face of uncertainty, control delays, or limited information flow at the human-machine interface. In conjunction with her research the PI will work to engage students at an early age in the exploration of the rich field of robotics. To that end, she will develop hands-on instructional modules for teaching elementary and middle school students about robotics using low-cost materials and deploy them locally for the benefit of students under-represented in science, technology, engineering, and mathematics fields. She will also develop an interactive exhibit for a science museum on robotic hands deploy it locally for the benefit of school-aged children and the general public in the metropolitan Phoenix area.

每个人的指尖有大约2000个触觉传感器。 这些传感器的刺激触发由脊柱介导的反应性抓握反应。 与人手的灵巧能力相比，机器人在非结构化环境中的操作能力是粗糙的。 当由人类操作员控制时，机器人操纵器进一步受到人机界面处的受限信息流（命令和感测）的限制。 所有的人机系统，从外科手术机器人到神经假体，都必须解决通信延迟的关键问题，根据人与机器之间的距离，通信延迟的范围可以从不到一秒到几个小时。 对于人工操作，即使延迟一秒钟也可能导致不良事件，例如开放性切口出血增加或挫折增加，并最终停用先进的假体。 从生物学中得到启示，自主的低水平反射可以在没有人类参与的情况下检测刺激并在机器人手中实施纠正反应，这可以为人机系统的通信、信息处理和决策赢得时间。 PI的一个长期研究目标是利用抓握反射算法原语、人工触觉传感器和受人手启发的可推广的抓握策略算法来推进机器人操作。 在这个项目中，她将专注于理解是什么驱动了低水平的反应性抓握反应，人机性能如何从类似的自主原语的实施中受益，以及机器人手可以学习什么抓握策略。 这项工作的贡献将包括在人类手中的反应性抓握反应的表征，开发人类启发的抓握反射算法原语和触觉传感器的机器人手，和学习算法的发展，自主提取一般把握机器人手的政策。 研究成果将增强我们对人类手部抓取原语的基本理解，为灵巧操作提供基础，并通过抓取反射算法原语和提取抓取策略的学习算法来改善机器人手部的功能。这项研究将通过使机器人抓取能够动态控制内收/外展度来改变人工操作，仿生触觉传感器的自由和使用，从而彻底改变了用于非结构化，访问受限或不安全环境的机器人操纵器（包括太空、水下、军事、救援、手术、辅助、康复和假肢），这些领域在面对不确定性、控制延迟、或人机界面上的有限信息流。 结合她的研究，PI将致力于让学生在很小的时候就参与机器人技术丰富领域的探索。 为此，她将开发实践教学模块，使用低成本的材料向小学和中学生教授机器人技术，并将其部署在当地，以造福科学、技术、工程和数学领域代表性不足的学生。 她还将为科学博物馆开发一个关于机器人手的互动展览，并将其部署在当地，以造福凤凰城大都市地区的学龄儿童和公众。