CDI-Type II: Collaborative Research: Cyber-Amplified Bioinspiration in Robotics

CDI-Type II：协作研究：机器人技术中的网络放大生物启发

• 批准号: 1028319
• 负责人: Robert Full
• 金额: $ 71.21万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1028319&HistoricalAwards=false
• 关键词: CDI; Type; II; Collaborative; Research

Intellectual MeritThis project mixes results and methods from cognitive psychology, computational vision and learning, neuromechanical systems biology, and robotics to develop a computer assisted environment for studying animal sensorimotor strategies, discovering how they undergird animal cognitive capabilities, and using those insights to inspire new algorithms for robot navigation, localization and situational awareness. We observe live, intact, highly mobile terrestrial invertebrate predators such as ghost crabs, desert scorpions and tiger beetles in carefully constructed habitats that challenge their ability to negotiate terrain and navigate space. We automate the collection, annotation and mathematical model extraction of their behavior from massive, parallel real-time recordings of visual, muscle, neural, and biomechanical recordings. We mine these data sets to develop intuitive hypotheses as well as formal mathematical representations of the basis on which these animals organize their own sensorimotor data streams to compile novel behaviors from previously consolidated constituents in a process of autonomous mental development. We add numerous existing sensor suites to highly agile existing robot bodies and instantiate algorithmically the hypothesized animal models to develop supporting or refuting evidence that challenges and refines them.Broader ImpactsScientifically, the new computational tools and ideas we identify in the interrelations we set up promise a bridge between whole areas of disciplines that have long been divided by spatiotemporal scale and the concomitant gap in analytical tradition, terminology and methods. For example, the study of these complex competencies in simpler species offers a new glimpse at the building blocks of cognition in species more closely related to humans. From the perspective of technological invention, algorithms pioneered in this research could lend an animal-like quality to a machine?s proximal tenacity in engaging its environment and even its overall situational awareness within unstructured worlds. For example, the team is inspired to imagine what it might be like to have a search and rescue robot with the (taskable) capabilities of a ghost crab. From the perspective of training and education, the automated database collection and management tools developed in this project bring to a mass audience the conceptual and computational building blocks that have heretofore been the exclusive province of a small group of experts. For example, a universally accessible (?cloud-based?) tool for unifying the design, parsing, display, and cross comparison of robots and animals searchable at will from the most intimate to the broadest scale of design and operation would have a profound impact on the ability of teachers at many different levels to motivate the fascination and unity of both synthetic and biological science.

智力优势该项目融合了认知心理学、计算视觉和学习、神经机械系统生物学和机器人学的结果和方法，开发了一个计算机辅助环境，用于研究动物感觉运动策略，发现它们如何增强动物认知能力，并利用这些见解激发机器人导航、定位和态势感知的新算法。 我们在精心建造的栖息地中观察到活生生的、完整的、高度机动的陆地无脊椎动物捕食者，如幽灵蟹、沙漠蝎子和虎甲虫，这些栖息地挑战了它们穿越地形和空间导航的能力。我们从视觉、肌肉、神经和生物力学记录的大量并行实时记录中自动收集、注释和提取他们的行为的数学模型。 我们挖掘这些数据集，以开发直观的假设以及形式的数学表示，这些动物在此基础上组织自己的感觉运动数据流，以在自主心理发展过程中从先前巩固的成分中汇编出新的行为。 我们将许多现有的传感器套件添加到高度敏捷的现有机器人身体中，并通过算法实例化假设的动物模型，以开发支持或反驳挑战和完善它们的证据。更广泛的影响从科学上讲，我们在我们建立的相互关系中确定的新计算工具和想法有望在长期被时空尺度划分的整个学科领域以及随之而来的分析传统、术语和技术方面的差距之间架起一座桥梁。 方法。 例如，对较简单物种的这些复杂能力的研究为与人类关系更密切的物种的认知构建模块提供了新的视角。 从技术发明的角度来看，这项研究中首创的算法可以赋予机器在参与其环境甚至是非结构化世界中的整体态势感知方面的近端韧性，从而赋予机器类似动物的品质。 例如，该团队受到启发，想象拥有一个具有幽灵蟹（可任务）能力的搜救机器人会是什么样子。 从培训和教育的角度来看，该项目开发的自动化数据库收集和管理工具为广大受众带来了概念和计算构建块，而这些概念和计算构建块迄今为止一直是一小群专家的专属领域。例如，一个普遍可用的（基于云的）工具，用于统一机器人和动物的设计、解析、显示和交叉比较，可随意搜索从最亲密到最广泛的设计和操作规模，这将对许多不同级别的教师激发合成科学和生物科学的魅力和统一的能力产生深远的影响。