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

该项目开发新技术，使计划者能够自动从经验中学习，为机器人、CAD/CAM、虚拟人物动画和手术计划中出现的大型运动计划问题提供快速、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的高维状态空间，非线性动力学，通常具有接触和冲击，以及长时间视野。先前的方法试图通过在规划中嵌入大量的领域知识来有效地解决这些问题，但在很大程度上依赖于人类的专业知识来开发和利用这些知识。这个过程冗长乏味，容易出错，并且不能很好地扩展到没有明显结构的更难的问题。该项目将研究规划系统的自动化策略，从过去的经验中自动发现通用的解决方案结构，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训练和手动调整来自动优化新领域的规划策略。更广泛的影响：有效地解决大型规划问题的能力对许多知识领域都有无数的好处。但在人的层面上，这笔拨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一名本科生或硕士生实习生的机会。更广泛的传播工作将通过分发用于任务和运动规划的研究和教育软件库（PyTAMP），以及将软件与开源ROS和OMPL库集成来实现。研究将与本科和研究生阶段的机器人和人工智能课程以及K-12机器人推广教育相结合。

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.