RI: Small: To Ask or Not to Ask - A Foundation for the Optimization of Human-Robot Networks

RI：小：问还是不问——人机网络优化的基础

• 批准号: 1619376
• 负责人: Yasamin Mostofi
• 金额: $ 49.76万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619376&HistoricalAwards=false
• 关键词: RI; Small; Ask; Foundation; Optimization

A team of unmanned vehicles can have tremendous impact in areas such as disaster relief, emergency response, and national security. Human operators are an integral part of these missions. Given the complexity of the tasks, harshness of the operation environments, and resource limitations, a fundamental understanding of how humans and robots can best interact is crucial to a successful operation. This proposal introduces a new multidisciplinary approach for efficient human-robot collaboration on tasks that require a visual search in the presence of uncertainty. The proposed methodology for characterizing human visual performance also contributes to the field of human perception and cognitive psychology. This proposal also has a significant educational component targeting under-represented students of the central California area and UC Santa Barbara.A new methodology is proposed for predicting whether people can make a correct visual decision for a given sensory input. The approach is based on training convolutional neural networks using extensive input from Amazon Mechanical Turk workers. This foundational understanding of human visual performance will have significant implication for robotic field decision making. A new set of mathematical tools will be developed for robotic field operation, given a proper understanding of human visual capabilities. This enables a robot to properly decide when to seek human help, when to rely on itself, and when to sense more. Finally, the cost of communication is characterized, due to the space-varying nature of link quality, which can significantly affect seeking human help. The proposed theories and design paradigms will be validated using a robotic testbed.

一支无人驾驶车辆团队可以在救灾、应急响应和国家安全等领域产生巨大影响。 人类操作员是这些任务不可或缺的一部分。考虑到任务的复杂性、操作环境的恶劣性和资源的限制，对人类和机器人如何最好地互动的基本了解对于成功的操作至关重要。该提案引入了一种新的多学科方法，可在存在不确定性的情况下实现人机高效协作，完成需要视觉搜索的任务。 所提出的描述人类视觉表现的方法也对人类感知和认知心理学领域做出了贡献。该提案还具有重要的教育内容，针对加州中部地区和加州大学圣巴巴拉分校的代表性不足的学生。提出了一种新的方法来预测人们是否能够针对给定的感官输入做出正确的视觉决策。 该方法基于使用 Amazon Mechanical Turk 工作人员的大量输入来训练卷积神经网络。对人类视觉性能的基本理解将对机器人领域的决策产生重大影响。鉴于对人类视觉能力的正确理解，将为机器人现场操作开发一套新的数学工具。这使得机器人能够正确决定何时寻求人类帮助、何时依靠自身以及何时进行更多感知。最后，由于链路质量的空间变化性质，通信成本被表征，这会显着影响寻求人工帮助。所提出的理论和设计范例将使用机器人测试台进行验证。