CAREER: Learning Symbolic Representations for Robot Manipulation

职业：学习机器人操作的符号表示

• 批准号: 1844960
• 负责人: George Konidaris
• 金额: $ 55万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844960&HistoricalAwards=false
• 关键词: CAREER; Learning; Symbolic; Representations; Robot

Recent years have seen a dramatic improvement in the quality and cost of general-purpose robot hardware. However, programming that hardware to solve any non-trivial task is extremely hard. It would be far preferable if robots could plan to reach user-specified goals on their own, without requiring highly detailed programming. A key challenge here is dealing with the low-level details of sensing and perception, while also reasoning at a high-level about the task to be completed. This project aims to develop a framework that allows robots to learn how to manipulate objects, how to usefully represent those objects abstractly, and how to generalize across objects that appear different but have the same functionality (e.g., different microwaves). This project will develop new algorithms that will enable robots to reason and plan in complex scenarios in the real world; it could therefore substantially accelerate the deployment of complex robots in semi-structured environments like the home, hospitals, light manufacturing facilities, and space. This project aims to enable robots to autonomously learn reusable object-centric motor skills and the portable symbolic representations that support planning with those skills. Learning motor skills to manipulate, and abstract representations to reason about, objects in the world---while generalizing across objects of similar functionality---will enable robots to generate intelligent, goal-directed mobile manipulation behavior. The project will 1) design practical algorithms that discover motor skills for manipulating objects by interacting with them, 2) design algorithms for generalizing those skills across objects with different appearances but similar functionality, and 3) develop a theoretically sound framework for learning object-centric abstract representations that support goal-directed planning using those skills, and demonstrate its use on a mobile manipulation robot.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近年来，通用机器人硬件的质量和成本都有了显著的提高。然而，编程硬件来解决任何重要的任务是非常困难的。如果机器人能够计划自己达到用户指定的目标，而不需要非常详细的编程，那就更好了。这里的一个关键挑战是处理感知和感知的低层次细节，同时还要对要完成的任务进行高层次的推理。该项目旨在开发一个框架，允许机器人学习如何操作对象，如何有效地抽象表示这些对象，以及如何在看起来不同但具有相同功能的对象之间进行泛化（例如，不同的微波）。该项目将开发新的算法，使机器人能够在真实的世界中的复杂场景中进行推理和规划;因此，它可以大大加快复杂机器人在家庭、医院、轻型制造设施和太空等半结构化环境中的部署。该项目旨在使机器人能够自主学习可重复使用的以对象为中心的运动技能和支持这些技能规划的便携式符号表示。 学习运动技能来操纵，抽象表示来推理，世界上的物体-同时在具有类似功能的物体之间进行概括-将使机器人能够产生智能的、目标导向的移动的操纵行为。该项目将1）设计实用的算法，通过与它们交互来发现操纵对象的运动技能，2）设计算法，将这些技能推广到具有不同外观但功能相似的对象，以及3）开发一个理论上合理的框架，用于学习以对象为中心的抽象表示，支持使用这些技能进行目标导向的规划，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Autonomous Learning of Object-Centric Abstractions for High-Level Planning

用于高层规划的以对象为中心的抽象的自主学习

• 发表时间: 2022
• 期刊: Proceedings of the The Tenth International Conference on Learning Representations
• 作者: James, S;Rosman, B;Konidaris, G.D.
• 通讯作者: Konidaris, G.D.

Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces

度量空间中强化学习的粗粒度平滑度

• 发表时间: 2023
• 期刊: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics
• 作者: Gottesman, O;Asadi, K;Allen, C;Lobel, S;Konidaris, GD;Littman, ML
• 通讯作者: Littman, ML

Model-based Lifelong Reinforcement Learning with Bayesian Exploration

• DOI: 10.48550/arxiv.2210.11579
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Haotian Fu;Shangqun Yu;Michael S. Littman;G. Konidaris

Simultaneously Learning Transferable Symbols and Language Groundings from Perceptual Data for Instruction Following

• DOI: 10.15607/rss.2020.xvi.102
• 发表时间: 2020-07
• 期刊: Robotics: Science and Systems XVI
• 作者: N. Gopalan;Eric Rosen;G. Konidaris;Stefanie Tellex

Learning to Infer Kinematic Hierarchies for Novel Object Instances

• DOI: 10.1109/icra46639.2022.9811968
• 发表时间: 2021-10
• 期刊: 2022 International Conference on Robotics and Automation (ICRA)
• 作者: H. Abdul-Rashid;Miles Freeman;Ben Abbatematteo;G. Konidaris;Daniel Ritchie