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

RI：媒介：协作研究：基于经验的规划：终身规划框架

• 批准号: 1409549
• 负责人: Maxim Likhachev
• 金额: $ 34万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1409549&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Experience

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.

随着时间的推移，机器人需要改进它们的行为，但为了让人类预测它们的行为，它们会产生一致的行为，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，许多任务都是重复的，比如打开抽屉。该项目开发的技术通过将规划视为一个终身过程来解决这些问题，并利用人类环境的结构来提高效率，例如，抽屉通常以类似的方式打开。这项研究合作正在开发一个基于经验图的终身规划框架，旨在通过在解决类似规划任务时利用过去的经验来提高规划的性能。这个概念是新颖的，因为经验是用来指导启发式搜索，而不是仅仅用于重复或适应。使这成为可能的想法是一种新颖的基于启发式搜索的框架，它可以利用先前的经验，并且仍然提供对完整性和路径质量的严格保证。团队研究如何在规划过程中有效地利用经验，规划如何收集经验，如何剔除冗余的经验，以及如何从演示中获取经验。应用包括日常家庭任务和小批量生产任务。在这项合作研究中开发的软件正在集成到ROS的核心库之一SBPL库中。该项目还包括教育活动，以及有助于连接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。