STTR Phase I: A platform for reactive and adaptive motion generation for real-world manipulation

STTR 第一阶段：用于现实世界操纵的反应式和自适应运动生成平台

• 批准号: 1622958
• 负责人: Nathan Ratliff
• 金额: $ 22.5万
• 依托单位: Lula Robotics Inc.
• 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: 2016-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622958&HistoricalAwards=false
• 关键词: STTR; Phase; platform; reactive; adaptive

The broader impact/commercial potential of this project revolves around the democratization of extremely complex motion generation, perception, and control systems. Most roboticists today program without understanding heat transfer, motor controllers, and lowlevelmessage passing. Abstractions improve robot accessibility and enable organizations to leverage a broader range of expertise. This project will further the modern robotics abstraction to obviate specialized knowledge of low level task processes such as movement and vision across a variety of robots. Product driven companies are best suited to develop business logic and a thoughtful customerexperience, and most struggle to acquire suitable in-house talent to remain competitive with foundational robotics techniques. This system addresses fundamental behavioral prerequisites for applications ranging from collaborative robotics in manufacturing and fulfillment to assistive robotics, caretaking, and personal home robotics as well as Unmanned Aerial Vehicles (UAVs),autonomous vehicles, and entertainment robots. With higher level interfaces, programmers can more easily leverage their creativity and intuition enabling a new generation of applications unhindered by the present day difficulties of low level development. Beyond the commercial potential, these advantages are pervasive: researchers, students, and children alike, of all genders and ethnicities, will be empowered to interact with, program, and study robots at a substantially higher level.This Small Business Innovation Research (SBIR) Phase I project will address the increasing need for a higher level interface to robots that abstracts away perception, motion generation, and control. Many competing techniques for motion generation and perception populate the literature, but recently optimization has become a common unifying theme playing a prominent role in continuously running and adapting behavior with the promise of speed and generality. This project will utilize state of the art technology in continuous motion optimization and modern real time vision and tracking techniques to build an integrated system appropriate for use across multiple robots. These techniques involve optimization, Riemannian geometry, low level control, online learning and more; many components are well suited to generalizing across robotic platforms, but questions remain regarding processing speed, robustness, and the utility ofexisting modeling tools. This project will assess the viability of such a higher level system by studying the latencies of reactions, precision of manipulation, and its long term robustness especially in the context of collaborative robotics for manufacturing using evaluations across multiple physical robotic platforms. The technical results will illuminate the role of optimization inmotion generation and perception for real world productionizable systems and uncover any remaining scientific question that must be addressed.

该项目更广泛的影响/商业潜力围绕着极其复杂的运动生成，感知和控制系统的民主化。今天，大多数机器人专家在编程时并不了解热传递、电机控制器和底层信息传递。抽象提高了机器人的可访问性，并使组织能够利用更广泛的专业知识。该项目将进一步推进现代机器人技术的抽象，以获得各种机器人的运动和视觉等低级任务过程的专业知识。产品驱动型公司最适合开发业务逻辑和周到的客户体验，大多数公司都在努力获得合适的内部人才，以保持基础机器人技术的竞争力。该系统解决了从制造和履行中的协作机器人到辅助机器人、护理和个人家庭机器人以及无人机（UAV）、自动驾驶汽车和娱乐机器人等应用的基本行为先决条件。通过更高级别的接口，程序员可以更容易地利用他们的创造力和直觉，使新一代的应用程序不受当前低级别开发困难的阻碍。除了商业潜力之外，这些优势也是无处不在的：研究人员、学生和儿童，无论性别和种族，都将能够在更高的水平上与机器人进行交互、编程和研究。这个小型企业创新研究（SBIR）第一阶段项目将解决对机器人更高层次接口的日益增长的需求，这些接口将抽象感知、运动生成和控制。许多运动生成和感知的竞争技术充斥着文献，但最近优化已成为一个共同的统一主题，在持续运行和适应行为中发挥着突出的作用，并具有速度和通用性的承诺。该项目将利用最先进的技术在连续运动优化和现代真实的时间视觉和跟踪技术，建立一个综合系统，适用于多个机器人。这些技术涉及优化，黎曼几何，低层次控制，在线学习和更多;许多组件非常适合于在机器人平台上通用化，但关于处理速度、鲁棒性和现有建模工具的实用性仍存在问题。该项目将通过研究反应的延迟，操作的精度及其长期鲁棒性来评估这种更高级别系统的可行性，特别是在使用多个物理机器人平台进行评估的协作机器人制造的背景下。技术成果将阐明优化在真实的世界可生产系统的运动生成和感知中的作用，并揭示任何必须解决的剩余科学问题。