RI: Medium: Collaborative Research: Experience-Based Planning: A Framework for Lifelong Planning

机器人需要随着时间的推移改善其行为，但要产生一致的行为，以便让人类预测其行为，这对于建立对它们行为的信任甚至与它们合作是必要的。此外，许多任务重复，例如打开抽屉。本研究课题将规划视为终身的过程，并利用抽屉的打开方式等人类环境的结构来提高效率，从而开发解决这些问题的技术。本研究合作开发基于经验图的终身规划框架，旨在通过利用过去解决类似规划任务时的经验，随着时间的推移提高规划性能。这个概念是新颖的，因为经验被用来指导启发式搜索，而不是仅仅用于重放或适应。使这成为可能的想法是一种新的启发式搜索为基础的框架，可以利用以前的经验，仍然提供严格的保证完整性和路径质量。该团队研究如何在规划过程中有效利用经验，规划应如何收集经验，如何修剪冗余经验以及如何从演示中获得经验。应用包括日常家务和小批量制造任务。在这项合作研究中开发的软件正在集成到SBPL库中，SBPL库是ROS的核心库之一。该项目还包括教育活动，以及有助于弥合机器人和人工智能研究社区的活动，这是两个独立的社区，尽管他们对自主系统有共同的兴趣。

Robots need to improve their behavior over time, yet produce consistent behavior in order to allow humans to predict their actions, which is necessary to develop trust in their behavior or even cooperate with them. Furthermore, many tasks repeat, such as opening drawers. This project develops technology that addresses these issues by viewing planning as a lifelong process and exploiting the structure of human environments for efficiency, for example that drawers typically open in similar ways.This research collaboration is developing a framework for lifelong planning based on experience graphs that aims to improve performance of planning over time by exploiting past experiences when solving similar planning tasks. The concept is novel because experiences are used to guide the heuristic search as opposed to be used for mere replay or adaptation. The idea that makes this possible is a novel heuristic search-based framework that can take advantage of prior experiences and still provide rigorous guarantees on completeness and path quality. The team studies how experiences can be utilized effectively during planning, how planning should gather experiences, how it should prune redundant experiences and how it can obtain experiences from demonstrations. Applications include everyday household tasks and low-volume manufacturing tasks. The software developed in this collaborative research is being integrated into the SBPL library, one of the core libraries in ROS. The project also incorporates educational activities as well as activities that help to bridge the research communities in robotics and artificial intelligence, two separate communities despite their common interest in autonomous systems.