NRI-Small: Context-Driven Haptic Inquiry of Objects Based on Task Requirements for Artificial Grasp and Manipulation

NRI-Small：基于人工抓取和操纵任务要求的上下文驱动的物体触觉查询

• 批准号: 1208519
• 负责人: Veronica Santos
• 金额: $ 65.15万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2014-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208519&HistoricalAwards=false
• 关键词: NRI; Small; Context; Driven; Haptic

PI: Santos, VeronicaProposal Number: 1208519Intellectual Merit: Human-like dexterous manipulation is featured prominently as a grand challenge in the 2009 Roadmap for U.S. Robotics' report. Human dexterity relies heavily on tactile sensation and is influenced by proprioceptive and visual feedback. The proposed work aims to advance artificial manipulators by integrating a new class of multimodal tactile sensors with anthropomorphic artificial hands and developing generalizable routines for context-driven haptic inquiry of objects based on task requirements for artificial grasp and manipulation. A primary goal is the development of capabilities for a robot hand to efficiently learn about objects in its unstructured environment through touch, specifically for cases where computer vision would fail to provide critical information about the physical hand-object interactions. While computer vision provides preliminary information about an object and its environment, vision alone cannot provide all essential information necessary for successful physical hand-object interactions. This is especially true when digits are occluded by the grasped object, and when the hand-object interaction is completely out of view. Inspiration for the haptic inquiry framework will be drawn from a suite of human haptic exploration procedures. In contrast to haptic exploration, haptic inquiry will require that the order and time spent on each exploratory procedure depend on task goals. The order and type of questions to be asked haptically will be context-dependent and designed to yield high-level, task-directed information at a low cost of inquiry. The weight given to each mode of tactile sensing (force, vibration, temperature) will also be tuned according to the context of the task.This proposal aims to strengthen the robustness of co-robot systems by developing a framework for context-driven, task-directed haptic inquiry that integrates multi-digit tactile and proprioception data in a task-appropriate manner. The framework will be developed and deployed on an anthropomorphic robot hand outfitted with a new class of commercially-available multimodal tactile sensors. The work is transformative because it will enable co-robot systems to remain functional even in the absence of visual feedback, which is typically the primary form of feedback for robotic systems. The long-term research objective of this proposal is to reduce the cognitive burden on the user of an artificial manipulator. Broader Impacts: The proposed translational research could enhance the functional capabilities of co-robot systems in which humans use artificial manipulators to work in unstructured, unsafe, or limited access environments (prosthetic, rehabilitative, assistive, space, underwater, military, rescue, surgery). The proposed work could benefit the human user of a co-robot system by empowering the robot with the ability to control low-level perception-action loops autonomously without burdening the human. The ROS operating system may be used to simulate and control an anthropomorphic robot hand outfitted with commercially-available tactile sensors using commercially-available actuators. Custom source code (C, MATLAB, ROS) and an open source haptic library for a commercially-available tactile sensor (suitable for data mining) will be made publicly available for the benefit and advancement of the robotics community.

主要研究者：桑托斯，维罗尼卡提案编号：1208519智力优点：在2009年美国机器人技术路线图报告中，像人类一样灵巧的操作是一个突出的挑战。人类的灵巧性在很大程度上依赖于触觉，并受到本体感受和视觉反馈的影响。拟议的工作旨在推进人工机械手集成一类新的多模态触觉传感器与拟人假手和开发通用例程的上下文驱动的触觉查询的对象的基础上，人工把握和操作的任务要求。主要目标是开发机器人手通过触摸有效地了解其非结构化环境中的对象的能力，特别是对于计算机视觉无法提供有关物理手-对象交互的关键信息的情况。虽然计算机视觉提供了关于物体及其环境的初步信息，但视觉本身无法提供成功的物理手-物体交互所需的所有基本信息。当手指被抓握的物体遮挡时，以及当手-物体交互完全在视野之外时，尤其如此。触觉查询框架的灵感将来自一套人类触觉探索程序。与触觉探索相反，触觉查询将要求每个探索程序所花费的顺序和时间取决于任务目标。触觉提问的顺序和类型将取决于上下文，旨在以较低的查询成本获得高层次的任务导向信息。每个模式的触觉传感（力，振动，温度）的权重也将根据上下文的task.This建议的目的是加强合作机器人系统的鲁棒性开发一个框架，上下文驱动的，面向任务的触觉查询，集成多位数的触觉和本体感觉数据在一个任务适当的方式。该框架将被开发和部署在一个拟人机器人手上，配备了一类新的商用多模态触觉传感器。这项工作具有变革性，因为它将使协作机器人系统即使在没有视觉反馈的情况下也能保持功能，而视觉反馈通常是机器人系统的主要反馈形式。该提案的长期研究目标是减少人工机械手用户的认知负担。更广泛的影响：拟议的转化研究可以增强协作机器人系统的功能能力，其中人类使用人工操纵器在非结构化，不安全或限制访问的环境中工作（假肢，康复，辅助，太空，水下，军事，救援，手术）。 所提出的工作可以通过赋予机器人自主控制低级别感知-动作循环的能力而不加重人类负担来使合作机器人系统的人类用户受益。ROS操作系统可用于使用市售致动器来模拟和控制配备有市售触觉传感器的拟人机器人手。自定义源代码（C，MATLAB，ROS）和一个开源触觉库的商业触觉传感器（适合数据挖掘）将公开提供的利益和机器人社区的进步。