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Text2Motion: from natural language instructions to feasible plans

Authors:

Christopher Agia,

Toki Migimatsu,

Jeannette BohgAuthors Info & Claims

Autonomous Robots, Volume 47, Issue 8

Pages 1345 - 1365

https://doi.org/10.1007/s10514-023-10131-7

Published: 14 November 2023 Publication History

Abstract

We propose Text2Motion, a language-based planning framework enabling robots to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural language instruction, our framework constructs both a task- and motion-level plan that is verified to reach inferred symbolic goals. Text2Motion uses feasibility heuristics encoded in Q-functions of a library of skills to guide task planning with Large Language Models. Whereas previous language-based planners only consider the feasibility of individual skills, Text2Motion actively resolves geometric dependencies spanning skill sequences by performing geometric feasibility planning during its search. We evaluate our method on a suite of problems that require long-horizon reasoning, interpretation of abstract goals, and handling of partial affordance perception. Our experiments show that Text2Motion can solve these challenging problems with a success rate of 82%, while prior state-of-the-art language-based planning methods only achieve 13%. Text2Motion thus provides promising generalization characteristics to semantically diverse sequential manipulation tasks with geometric dependencies between skills. Qualitative results are made available at https://sites.google.com/stanford.edu/text2motion.

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Index Terms

Text2Motion: from natural language instructions to feasible plans
1. Computer systems organization
  1. Embedded and cyber-physical systems

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cover image Autonomous Robots

Autonomous Robots Volume 47, Issue 8

Dec 2023

598 pages

ISSN:0929-5593

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Publication History

Published: 14 November 2023

Accepted: 31 July 2023

Received: 02 May 2023

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Ji XPan ZGao XPan J(2024)Text-Guided Synthesis of Crowd AnimationACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657516(1-11)Online publication date: 13-Jul-2024
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Mahadevan KChien JBrown NXu ZParada CXia FZeng ATakayama LSadigh DGrollman DBroadbent EJu WSoh HWilliams T(2024)Generative Expressive Robot Behaviors using Large Language ModelsProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634999(482-491)Online publication date: 11-Mar-2024
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Kim YKim DChoi JPark JOh NPark D(2024)A survey on integration of large language models with intelligent robotsIntelligent Service Robotics10.1007/s11370-024-00550-517:5(1091-1107)Online publication date: 1-Sep-2024
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Qi ZDong RZhang SGeng HHan CGe ZYi LMa K(2024)ShapeLLM: Universal 3D Object Understanding for Embodied InteractionComputer Vision – ECCV 202410.1007/978-3-031-72775-7_13(214-238)Online publication date: 29-Sep-2024
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