RI: Small: Discovery and Reuse of Domain Knowledge in Large Motion Planning Systems

RI：小型：大型运动规划系统中领域知识的发现和重用

• 批准号: 1502600
• 负责人: Kris Hauser
• 金额: $ 19.5万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-02 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1502600&HistoricalAwards=false
• 关键词: RI; Small; Discovery; Reuse; Domain

This project, developing new techniques for enabling planners to automatically learn from experience, offers fast, high-quality solutions to very large motion planning problems that arise in robotics, CAD/CAM, animation of virtual characters, and surgical planning. These problems are challenging because they require searching high-dimensional state spaces with complex geometric constraints, nonlinear dynamics, often with contact and impact, and long time horizons. Prior approaches have sought to solve these problems efficiently by embedding a great deal of domain knowledge into planning, but have relied heavily on human expertise to develop and exploit this knowledge. This process is tedious, error-prone, and does not scale well to harder problems that do not possess an obvious structure. This project will investigate automated strategies for planning systems to automatically discover common solution structures from past experience and to reuse this knowledge in new problems, with the ultimate goal of demonstrating a system that automatically optimizes planning strategies for a novel domain with minimal training and hand tuning.Broader Impacts: The ability to solve large planning problems efficiently has myriad benefits to many fields of knowledge. But on a human level, this grant will provide the opportunity to recruit an undergraduate or Master's student intern from a minority-serving institution each summer, in cooperation with the Alliance for the Advancement of African-American Researchers in Computing (A4RC). Broader dissemination of the work will be achieved by distributing a research and educational software library for task-and-motion planning (PyTAMP), and integrating software with the open-source ROS and OMPL libraries. Research will be integrated with education in robotics and AI courses at the undergraduate and graduate level, as well as in K-12 robotics outreach.

该项目开发新技术，使规划人员能够自动从经验中学习，为机器人、CAD/CAM、虚拟角色动画和手术规划中出现的大型运动规划问题提供快速、高质量的解决方案。这些问题具有挑战性，因为它们需要搜索具有复杂几何约束、非线性动力学（通常具有接触和影响）以及长时间范围的高维状态空间。先前的方法试图通过将大量领域知识嵌入到规划中来有效地解决这些问题，但在很大程度上依赖于人类专业知识来开发和利用这些知识。这个过程是乏味的、容易出错的，并且不能很好地扩展到不具有明显结构的更困难的问题。该项目将研究规划系统的自动化策略，以从过去的经验中自动发现常见的解决方案结构，并在新问题中重用这些知识，最终目标是展示一个系统，该系统可以通过最少的培训和手动调整自动优化新领域的规划策略.更广泛的影响：有效解决大型规划问题的能力对许多知识领域都有无数好处。但在人的层面上，这笔赠款将提供每年夏天与非裔美国计算机研究人员促进联盟 (A4RC) 合作，从少数族裔服务机构招募本科生或硕士生实习生的机会。通过分发用于任务和运动规划的研究和教育软件库 (PyTAMP)，并将软件与开源 ROS 和 OMPL 库集成，可以更广泛地传播这项工作。研究将与本科生和研究生的机器人和人工智能课程教育以及 K-12 机器人推广相结合。