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

该项目更广泛的影响/商业潜力围绕着极其复杂的动作生成、感知和控制系统的民主化。如今，大多数机器人专家在不了解传热学、电机控制器和底层信息传递的情况下进行编程。抽象提高了机器人的可访问性，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学的抽象，以消除对各种机器人的运动和视觉等低级任务过程的专门知识。产品驱动型公司最适合开发业务逻辑和深思熟虑的客户体验，大多数公司都难以获得合适的内部人才，以保持与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、看护和个人家庭机器人以及无人驾驶飞行器(UAV)、自动车辆和娱乐机器人等应用的基本行为先决条件。有了更高级别的界面，程序员可以更容易地利用他们的创造力和直觉，使新一代应用程序不受当今低级开发困难的阻碍。除了商业潜力，这些优势是无处不在的：研究人员、学生和儿童，无论性别和种族，都将被授权在更高的水平上与机器人互动、编程和研究机器人。这个小型企业创新研究(SBIR)第一阶段项目将满足日益增长的对机器人更高级别接口的需求，这些接口可以抽象出感知、动作生成和控制。许多相互竞争的运动生成和感知技术充斥在文献中，但最近优化已经成为一个共同的统一主题，在连续运行和适应行为方面发挥着突出的作用，并承诺速度和通用性。该项目将利用最先进的连续运动优化技术和现代实时视觉和跟踪技术来构建一个适合跨多个机器人使用的集成系统。这些技术涉及优化、黎曼几何、低级控制、在线学习等许多组件非常适合在机器人平台上推广，但处理速度、健壮性和现有建模工具的实用性仍存在问题。这个项目将通过研究反应的潜伏期、操作的精确度及其长期的健壮性来评估这样一个更高级别系统的可行性，特别是在用于制造的协作机器人的背景下，使用跨多个物理机器人平台的评估。技术成果将阐明优化InMotion生成和感知对于现实世界可生产系统的作用，并揭示任何必须解决的剩余科学问题。

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.