CAREER: Learning Symbolic Representations for Robot Manipulation

职业：学习机器人操作的符号表示

基本信息

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

项目摘要

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有了显着提高。然而，对硬件进行编程来解决任何重要的任务是极其困难的。如果机器人能够自行计划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级细节，同时对要完成的任务进行高级推理。该项目旨在开发一个框架，使机器人能够学习如何操纵物体，如何有效地抽象地表示这些物体，以及如何对看似不同但具有相同功能的物体（例如不同的微波炉）进行概括。该项目将开发新的算法，使机器人能够在现实世界的复杂场景中进行推理和规划；因此，它可以大大加速复杂机器人在家庭、医院、轻工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以及支持利用这些技能进行规划的便携式符号表示。学习运动技能来操纵世界上的物体，并抽象表示来推理世界上的物体——同时概括具有相似功能的物体——将使机器人能够产生智能的、目标导向的移动操纵行为。该项目将 1) 设计实用算法，通过与物体交互来发现操纵物体的运动技能，2) 设计算法，以在外观不同但功能相似的物体上推广这些技能，3) 开发一个理论上合理的框架，用于以物体为中心的学习使用这些技能支持目标导向规划的抽象表示，并展示其在移动操纵机器人上的使用。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

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
作者：
N. Gopalan;Eric Rosen;G. Konidaris;Stefanie Tellex
通讯作者：
N. Gopalan;Eric Rosen;G. Konidaris;Stefanie Tellex

Model-based Lifelong Reinforcement Learning with Bayesian Exploration

DOI：
10.48550/arxiv.2210.11579
发表时间：
2022-10
期刊：
ArXiv
影响因子：
0
作者：
Haotian Fu;Shangqun Yu;Michael S. Littman;G. Konidaris
通讯作者：
Haotian Fu;Shangqun Yu;Michael S. Littman;G. Konidaris

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

DOI：
发表时间：
2023
期刊：
Proceedings of the 26th International Conference on Artificial Intelligence and Statistics
影响因子：
0
作者：
Gottesman, O;Asadi, K;Allen, C;Lobel, S;Konidaris, GD;Littman, ML
通讯作者：
Littman, ML

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

DOI：
发表时间：
2022
期刊：
Proceedings of the The Tenth International Conference on Learning Representations
影响因子：
0
作者：
James, S;Rosman, B;Konidaris, G.D.
通讯作者：
Konidaris, G.D.

Q-functionals for Value-Based Continuous Control