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Robotics Research pp 291–307Cite as

Demonstration-Guided Motion Planning

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Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 100))

Abstract

We present demonstration-guided motion planning (DGMP), a new frame-work for planning motions for personal robots to perform household tasks. DGMP combines the strengths of sampling-based motion planning and robot learning from demonstrations to generate plans that (1) avoid novel obstacles in cluttered environments, and (2) learn and maintain critical aspects of the motion required to successfully accomplish a task. Sampling-based motion planning methods are highly effective at computing paths from start to goal configurations that avoid obstacles, but task constraints (e.g. a glass of water must be held upright to avoid a spill) must be explicitly enumerated and programmed. Instead, we use a set of expert demonstrations and automatically extract time-dependent task constraints by learning low variance aspects of the demonstrations, which are correlated with the task constraints. We then introduce multi-component rapidly-exploring roadmaps (MC-RRM), a sampling-based method that incrementally computes a motion plan that avoids obstacles and optimizes a learned cost metric. We demonstrate the effectiveness of DGMP using the Aldebaran Nao robot performing household tasks in a cluttered environment, including moving a spoon full of sugar from a bowl to a cup and cleaning the surface of a table.

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Acknowledgment

This research was supported in part by the National Science Foundation (NSF) under awards #IIS-0905344 and #IIS-1117127 and by the National Institutes of Health (NIH) under grant #R21EB011628. The authors thank Jenny Womack from the Dept. of Allied Health Sciences for her input and John Thomas and Herman Towles from the Dept. of Computer Science for their help with the experiment setup.

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Authors and Affiliations

  1. Department of Computer Science, The University of North Carolina, Chapel Hill, NC, 27599, USA

    Gu Ye & Ron Alterovitz

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  1. Gu Ye
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  2. Ron Alterovitz
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Correspondence to Gu Ye .

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Editors and Affiliations

  1. Robotics and Intelligent Machines, Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Henrik I. Christensen

  2. Department of Computer Science, Stanford University, Stanford, California, USA

    Oussama Khatib

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Ye, G., Alterovitz, R. (2017). Demonstration-Guided Motion Planning. In: Christensen, H., Khatib, O. (eds) Robotics Research . Springer Tracts in Advanced Robotics, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-29363-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-29363-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29362-2

  • Online ISBN: 978-3-319-29363-9

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