A review of methodologies for natural-language-facilitated human-robot cooperation

被引:24
|
作者
Liu, Rui [1 ]
Zhang, Xiaoli [2 ]
机构
[1] Carnegie Mellon Univ, RI, Pittsburgh, PA 15213 USA
[2] Colorado Sch Mines, IRSL, Golden, CO 80401 USA
来源
关键词
Natural language; human-robot cooperation; NL instruction understanding; NL-based execution plan generation; knowledge-world mapping; MODEL; RECOGNITION; COMMAND;
D O I
10.1177/1729881419851402
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Natural-language-facilitated human-robot cooperation refers to using natural language to facilitate interactive information sharing and task executions with a common goal constraint between robots and humans. Recently, natural-language-facilitated human-robot cooperation research has received increasing attention. Typical natural-language-facilitated human-robot cooperation scenarios include robotic daily assistance, robotic health caregiving, intelligent manufacturing, autonomous navigation, and robot social accompany. However, a thorough review, which can reveal latest methodologies of using natural language to facilitate human-robot cooperation, is missing. In this review, we comprehensively investigated natural-language-facilitated human-robot cooperation methodologies, by summarizing natural-language-facilitated human-robot cooperation research as three aspects (natural language instruction understanding, natural language-based execution plan generation, knowledge-world mapping). We also made in-depth analysis on theoretical methods, applications, and model advantages and disadvantages. Based on our paper review and perspective, future directions of natural-language-facilitated human-robot cooperation research were discussed.
引用
收藏
页数:17
相关论文
共 50 条
  • [41] Situated Human-Robot Collaboration: predicting intent from grounded natural language
    Brawer, Jake
    Mangin, Olivier
    Roncone, Alessandro
    Widder, Sarah
    Scassellati, Brian
    2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2018, : 827 - 833
  • [42] Grounding Spatial Relations in Natural Language by Fuzzy Representation for Human-Robot Interaction
    Tan, Jiacheng
    Ju, Zhaojie
    Liu, Honghai
    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2014, : 1743 - 1750
  • [43] Navigation by natural human-Robot Interaction
    Muhlbauer, Quirin
    Xu, Lingting
    Bauer, Andrea
    Klasing, Klaas
    Lidoris, Georgios
    Rohrmuller, Florian
    Sosnowski, Stefan
    Kuhnlenz, Kolja
    Wollherr, Dirk
    Buss, Martin
    AT-AUTOMATISIERUNGSTECHNIK, 2010, 58 (11) : 647 - 656
  • [44] Modeling of Natural Human-Robot Encounters
    Bergstrom, Niklas
    Kanda, Takayuki
    Miyashita, Takahiro
    Ishiguro, Hiroshi
    Hagita, Norihiro
    2008 IEEE/RSJ INTERNATIONAL CONFERENCE ON ROBOTS AND INTELLIGENT SYSTEMS, VOLS 1-3, CONFERENCE PROCEEDINGS, 2008, : 2623 - +
  • [45] Online Robot Teaching With Natural Human-Robot Interaction
    Du, Guanglong
    Chen, Mingxuan
    Liu, Caibing
    Zhang, Bo
    Zhang, Ping
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2018, 65 (12) : 9571 - 9581
  • [46] Human Behavior Analysis in Human-Robot Cooperation with AR Glasses
    Owaki, Koichi
    Techasarntiku, Nattaon
    Shimonishi, Hideyuki
    2023 IEEE INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY, ISMAR, 2023, : 20 - 28
  • [47] Online Stability in Human-Robot Cooperation with Admittance Control
    Dimeas, Fotios
    Aspragathos, Nikos
    IEEE TRANSACTIONS ON HAPTICS, 2016, 9 (02) : 267 - 278
  • [48] On the manipulation of articulated objects in human-robot cooperation scenarios
    Capitanelli, Alessio
    Maratea, Marco
    Mastrogiovanni, Fulvio
    Vallati, Mauro
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2018, 109 : 139 - 155
  • [49] Matching robot appearance and behavior to tasks to improve human-robot cooperation
    Goetz, J
    Kiesler, S
    Powers, A
    RO-MAN 2003: 12TH IEEE INTERNATIONAL WORKSHOP ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, PROCEEDINGS, 2003, : 55 - 60
  • [50] Objectification of Assembly Planning for the Implementation of Human-Robot Cooperation
    Mueller, Rainer
    Peifer, Richard
    Mailahn, Ortwin
    ADVANCES IN ARTIFICIAL INTELLIGENCE, SOFTWARE AND SYSTEMS ENGINEERING, 2019, 787 : 24 - 34