NRI: Collaborative Research: A Framework for Hierarchical, Probabilistic Planning and Learning

NRI：协作研究：分层、概率规划和学习的框架

• 批准号: 1637614
• 负责人: Stefanie Tellex
• 金额: $ 54.27万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637614&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Framework; Hierarchical

This project is an effort to create a unified framework for solving very large problems with uncertain states and actions, such as manipulator robots acting in real-world environments. The results may have especially great promise for assistive technologies, including autonomous robots that can be used by elderly and disabled populations to aid them in their daily activities. The proposed integrated framework will represent, apply, and learn hierarchical domain knowledge, and will include the ability to transfer knowledge from simpler problems to more complex ones. The research will enable autonomous agents to develop a structured representation of complex domains based on experience. The agents will use learned representations to interpret natural language commands for both low-level and high-level requests. The technical focus is enabling tractable planning in large, uncertain domains by generating and leveraging probabilistic domain knowledge at multiple levels of abstraction. Agents will autonomously create layered representations in which the layers build on one another to produce complex behaviors. Agents will learn to perform useful behaviors, such as navigating using low-level sensor feedback or assembling complex objects such as a bridge or a table. The key technical contributions will be methods for (1) planning in large state/action spaces using the abstract object-oriented Markov decision process (AMDP) model, a new formalism for representing probabilistic domain knowledge at multiple levels of abstraction; (2) learning hierarchical task knowledge in the form of AMDPs; and (3) interpreting natural language commands at multiple levels of abstraction by mapping to the learned hierarchical structure. The formalism will be demonstrated and validated in several domains, including a simulated "cleanup" toy domain, challenging and complex video games, and a robot manipulation task.

该项目是为了创建一个统一的框架，以解决不确定的状态和行动，例如在现实世界环境中作用的机器人机器人。 结果可能对辅助技术特别有希望，包括老年人和残疾人人群可以使用的自主机器人，以帮助他们进行日常活动。 所提出的集成框架将代表，应用和学习层次结构域知识，并将包括将知识从简单问题转移到更复杂问题的能力。该研究将使自主代理能够根据经验开发复杂领域的结构化表示。代理商将使用学习的表示形式来解释低级和高级请求的自然语言命令。 技术重点是通过在多个抽象层面上产生和利用概率域知识来实现​​大型，不确定的领域的可牵引计划。代理人将自主创建分层表示，其中层相互构建以产生复杂的行为。代理人将学会执行有用的行为，例如使用低级传感器反馈导航或组装复杂的对象，例如桥梁或桌子。 关键的技术贡献将是（1）使用面向对象的马尔可夫决策过程（AMDP）模型在大型状态/行动空间中进行计划的方法，这是一种新的形式主义，用于在多个抽象级别上代表概率领域知识； （2）以AMDP的形式学习层次任务知识； （3）通过映射到学到的层次结构来解释多个抽象级别的自然语言命令。形式主义将在几个领域进行演示和验证，包括模拟的“清理”玩具域，具有挑战性且复杂的视频游戏以及机器人操纵任务。

项目成果

Multi-Object Search using Object-Oriented POMDPs

• DOI: 10.1109/icra.2019.8793888
• 发表时间: 2019-01-01
• 期刊: 2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
• 作者: Wandzel, Arthur;Oh, Yoonseon;Tellex, Stefanie
• 通讯作者: Tellex, Stefanie

The Expected-Length Model of Options

期权的预期长度模型

• 发表时间: 2019
• 期刊: IJCAI
• 作者: David Abel*, John Winder*

State Abstractions for Lifelong Reinforcement Learning

• 发表时间: 2018-07
• 作者: David Abel;Dilip Arumugam;Lucas Lehnert;M. Littman

Near Optimal Behavior via Approximate State Abstraction

• 发表时间: 2016-06
• 作者: David Abel;D. E. Hershkowitz;M. Littman

Planning with State Abstractions for Non-Markovian Task Specifications

非马尔可夫任务规范的状态抽象规划

• 发表时间: 2019
• 期刊: Robotics science and systems
• 作者: Yoonseon Oh, Roma Patel