NRI: Collaborative Research: Shall I Touch This?: Navigating the Look and Feel of Complex Surfaces

NRI：协作研究：我应该触摸这个吗？：导航复杂表面的外观和感觉

• 批准号: 1427425
• 负责人: Trevor Darrell
• 金额: $ 60万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-15 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1427425&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Shall; Touch

This project improves autonomous robotic perception so that future co-robots can glance around any scene and accurately estimate how it would feel to grasp or step on all of the visible surfaces. Just as people do, robots should use such these physical predictions to guide their interactions with the world, for example avoiding dangerous ice patches on the ground when walking and driving, and adeptly anticipating the grasp force needed to pick up everything from ice cubes to stuffed animals. These research activities are accompanied by significant outreach efforts, including a new program on "Look and Touch Robotics" to get middle-school students, particularly those from under­represented groups, excited about computer science, engineering, and robotics. This program uses simple experiments to highlight the dual importance of visual and haptic information during interactions with physical objects, along with demonstrations of a robot showing visuo-­haptic intelligence. This project also integrates research and education by involving undergraduates in the research and via hands-on projects in the vision and robotics classes taught by the Principal Investigators.This research involves extensive collection of data from real objects and surfaces using both visual and haptic sensors. The recorded interactions are analyzed to uncover visual clues that can allow a robot to infer the physical characteristics of the surface, such as slipperiness, hardness, and roughness. This problem is addressed using deep learning, a recently developed approach that has been successful in enabling robots to visually recognize a wide variety of objects in diverse circumstances. The research team also builds the database of visuo-haptic recordings and the learned cross-modal sensory, and makes it available to other robotics researchers at the end of the project.

该项目改进了机器人的自主感知，以便未来的协作机器人可以环视任何场景，并准确估计抓住或踩在所有可见表面上的感觉，就像人类一样，机器人应该使用这些物理预测来指导它们。与世界的互动，例如在行走和驾驶时避免地面上的危险冰块，并熟练地预测拾取从冰块到毛绒玩具等所有物品所需的抓握力。这些研究活动伴随着重大的外展工作，包括一项新的研究。 “看和触摸”节目机器人技术”旨在让中学生，特别是那些来自弱势群体的学生，对计算机科学、工程和机器人技术感到兴奋。该项目使用简单的实验来强调视觉和触觉信息在与物理对象交互过程中的双重重要性，并演示该项目还整合了参与研究的本科生的研究和教育，以及通过首席研究员教授的视觉和机器人课程中的实践项目。这项研究涉及从真实物体和机器人中广泛收集数据。使用两者的表面视觉和触觉传感器对记录的交互进行分析，以发现视觉线索，使机器人能够推断出表面的物理特征，例如光滑度、硬度和粗糙度。这个问题可以通过深度学习来解决，这是一种最近开发的方法。已经成功地使机器人能够在不同的情况下视觉识别各种物体。该研究团队还建立了视觉触觉记录和学习的跨模式感觉的数据库，并在年底向其他机器人研究人员提供。这项目。