CAREER: Robots that Help People

职业：帮助人们的机器人

• 批准号: 1652561
• 负责人: Stefanie Tellex
• 金额: $ 54.94万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-15 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652561&HistoricalAwards=false
• 关键词: CAREER; Robots; Help; People

As robots become more prevalent, it is crucial to develop ways for people to collaborate with them. This proposal aims to create collaborative robots through a combination of communication, perception, and action. Existing approaches are typically tailored to specific applications; yet people want to talk to robots about everything they can see and do. To address such limitations, this project will create a unified framework to enable robots to communicate with people to learn their needs, plan how to achieve them, and then perceive and act in the world in order to meet those needs. The research will be demonstrated with robots that can assist with household tasks, such as cooking and cleaning, as well as in manufacturing settings, such as collaborative assembly. The project will expose many people to collaborative robotics through an internship program with local high schools, a regional robotics conference, and the Million Object Challenge.This project will create a model, the Human-Robot Collaborative Partially Observable Markov Decision Process, that enables robots to 1) automatically acquire object-oriented models of objects in the physical world; 2) communicate with people to understand their needs and how to meet them; and 3) act to change the world in ways that meet people's needs. Creating a unified framework requires bridging gaps between different aspects of the robotic system. This project focuses on creating a single probabilistic graphical model to represent the robot's states and actions in a hierarchical framework, allowing the robot to make plans that take into account its own uncertainty and to communicate with a person about everything it can see and everything it can do. Focusing on collaboration leads to reformulations of traditional problems in computer vision, planning, and natural language understanding enabling the robot to collaborate in new and more natural ways.

随着机器人变得越来越普遍，开发人们与它们合作的方式至关重要。该提案旨在通过通信、感知和行动的结合来创造协作机器人。现有的方法通常是针对特定的应用程序量身定制的；然而，人们想和机器人谈论他们所能看到和做的一切。为了解决这些限制，该项目将创建一个统一的框架，使机器人能够与人交流，了解他们的需求，计划如何实现这些需求，然后感知和行动世界，以满足这些需求。这项研究将用机器人来演示，这些机器人可以帮助完成家务，比如做饭和清洁，也可以在制造环境中帮助完成协作组装。该项目将通过当地高中的实习项目、区域机器人会议和百万对象挑战，让许多人接触到协作机器人。该项目将创建一个模型，即人-机器人协作部分可观察马尔可夫决策过程，使机器人能够1)自动获取物理世界中对象的面向对象模型；2)与人们沟通，了解他们的需求以及如何满足他们的需求；3)以满足人们需求的方式改变世界。创建一个统一的框架需要弥合机器人系统不同方面之间的差距。这个项目的重点是创建一个单一的概率图形模型，在一个分层框架中表示机器人的状态和动作，允许机器人在考虑自身不确定性的情况下制定计划，并与人沟通它所看到的一切和它所能做的一切。关注协作导致了对计算机视觉、规划和自然语言理解等传统问题的重新表述，使机器人能够以新的、更自然的方式进行协作。

项目成果

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

Spatial Language Understanding for Object Search in Partially Observed City-scale Environments

部分观测城市规模环境中对象搜索的空间语言理解

• DOI: 10.1109/ro-man50785.2021.9515426
• 发表时间: 2021
• 期刊: ICRA
• 作者: Zheng, Kaiyu;Bayazit, Deniz;Mathew, Rebecca;Pavlick, Ellie;Tellex, Stefanie
• 通讯作者: Tellex, Stefanie