research-article

The Motion Grammar: Analysis of a Linguistic Method for Robot Control

Authors:

Neil Dantam,

Mike StilmanAuthors Info & Claims

IEEE Transactions on Robotics, Volume 29, Issue 3

Pages 704 - 718

https://doi.org/10.1109/TRO.2013.2239553

Published: 01 June 2013 Publication History

Abstract

We present the Motion Grammar: an approach to represent and verify robot control policies based on context-free grammars. The production rules of the grammar represent a top-down task decomposition of robot behavior. The terminal symbols of this language represent sensor readings that are parsed in real time. Efficient algorithms for context-free parsing guarantee that online parsing is computationally tractable. We analyze verification properties and language constraints of this linguistic modeling approach, show a linguistic basis that unifies several existing methods, and demonstrate effectiveness through experiments on a 14-degree-of-freedom (DOF) manipulator interacting with 32 objects (chess pieces) and an unpredictable human adversary. We provide many of the algorithms discussed as Open Source, permissively licensed software. Many algorithms discussed in this paper implemented in our Motion Grammar Kit: http://www.golems.org/node/1224

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Wu HLi HFang XLuo X(2022)A survey on teaching workplace skills to construction robotsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117658205:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117658
Mandery CBorràs JJöchner MAsfour T(2021)Using language models to generate whole-body multi-contact motions2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2016.7759796(5411-5418)Online publication date: 11-Mar-2021
https://dl.acm.org/doi/10.1109/IROS.2016.7759796
Lioutikov RMaeda GVeiga FKersting KPeters J(2020)Learning attribute grammars for movement primitive sequencingInternational Journal of Robotics Research10.1177/027836491986827939:1(21-38)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1177/0278364919868279
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The Motion Grammar: Analysis of a Linguistic Method for Robot Control
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
2. Theory of computation
  1. Formal languages and automata theory

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cover image IEEE Transactions on Robotics

IEEE Transactions on Robotics Volume 29, Issue 3

June 2013

229 pages

ISSN:1552-3098

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IEEE Press

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Published: 01 June 2013

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Wu HLi HFang XLuo X(2022)A survey on teaching workplace skills to construction robotsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117658205:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117658
Mandery CBorràs JJöchner MAsfour T(2021)Using language models to generate whole-body multi-contact motions2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2016.7759796(5411-5418)Online publication date: 11-Mar-2021
https://dl.acm.org/doi/10.1109/IROS.2016.7759796
Lioutikov RMaeda GVeiga FKersting KPeters J(2020)Learning attribute grammars for movement primitive sequencingInternational Journal of Robotics Research10.1177/027836491986827939:1(21-38)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1177/0278364919868279
Wan WIgawa HHarada KOnda HNagata KYamanobe N(2019)A regrasp planning component for object reorientationAutonomous Robots10.1007/s10514-018-9781-y43:5(1101-1115)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10514-018-9781-y
Dantam NKingston ZChaudhuri SKavraki L(2018)An incremental constraint-based framework for task and motion planningInternational Journal of Robotics Research10.1177/027836491876157037:10(1134-1151)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1177/0278364918761570
Lee KLee SLee J(2018)Interactive character animation by learning multi-objective controlACM Transactions on Graphics10.1145/3272127.327507137:6(1-10)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1145/3272127.3275071
Lioutikov RMaeda GVeiga FKersting KPeters J(2018)Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives2018 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA.2018.8460190(1-8)Online publication date: 21-May-2018
https://dl.acm.org/doi/10.1109/ICRA.2018.8460190
Liu RZhang X(2018)Generating machine-executable plans from end-user's natural-language instructionsKnowledge-Based Systems10.1016/j.knosys.2017.10.023140:C(15-26)Online publication date: 15-Jan-2018
https://dl.acm.org/doi/10.1016/j.knosys.2017.10.023
Bajcsy RAloimonos YTsotsos J(2018)Revisiting active perceptionAutonomous Robots10.1007/s10514-017-9615-342:2(177-196)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s10514-017-9615-3
Hyun KLee KLee JGomes APurgathofer WJorge JLin M(2016)Motion grammars for character animationProceedings of the 37th Annual Conference of the European Association for Computer Graphics10.5555/3058909.3058924(103-113)Online publication date: 9-May-2016
https://dl.acm.org/doi/10.5555/3058909.3058924
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