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.

该项目提高了自主机器人的感知能力，使未来的协作机器人可以扫视任何场景，并准确地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他们与世界的互动，例如在行走和驾驶时避免地面上危险的冰块，并熟练地预测从冰块到填充动物所需的抓取力。这些研究活动伴随着重大的推广工作，包括一个新的“看和触摸机器人”计划，让中学生，特别是那些来自少数群体的学生，对计算机科学，工程和机器人感到兴奋。该程序使用简单的实验来突出视觉和触觉信息在与物理对象交互期间的双重重要性，沿着展示视觉-触觉智能的机器人的演示。 该项目还通过让本科生参与研究以及通过主要研究者教授的视觉和机器人课程中的实践项目来整合研究和教育。该研究涉及使用视觉和触觉传感器从真实的物体和表面收集大量数据。 分析记录的交互以发现视觉线索，这些线索可以允许机器人推断表面的物理特性，例如光滑度，硬度和粗糙度。 这个问题可以使用深度学习来解决，深度学习是最近开发的一种方法，它成功地使机器人能够在不同的环境中视觉识别各种各样的物体。 研究团队还建立了视觉触觉记录和学习的跨模态感觉的数据库，并在项目结束时提供给其他机器人研究人员。