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Policy Search on Aggregated State Space for Active Sampling

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Proceedings of the 2018 International Symposium on Experimental Robotics (ISER 2018)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 11))

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Abstract

We present an anytime [1] adaptive sampling technique that generates paths to efficiently measure and then mathematically model a scalar field by performing non-uniform measurements in a given region of interest.

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Author information

Authors and Affiliations

  1. Mobile Robotics Lab (MRL), Center for Intelligent Machines, McGill University, Montreal, QC, Canada

    Sandeep Manjanna & Gregory Dudek

  2. Amsterdam Machine Learning Lab (AMLAB), University of Amsterdam, Amsterdam, The Netherlands

    Herke Van Hoof

Authors
  1. Sandeep Manjanna
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  2. Herke Van Hoof
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  3. Gregory Dudek
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Corresponding author

Correspondence to Sandeep Manjanna .

Editor information

Editors and Affiliations

  1. Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA, USA

    Jing Xiao

  2. Karlsruhe Institute of Technology, Karlsruhe, Baden-Württemberg, Germany

    Torsten Kröger

  3. Department of Computer Science, Stanford University, Stanford, CA, USA

    Oussama Khatib

1 Electronic supplementary material

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Supplementary material 1 (mp4 32362 KB)

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Manjanna, S., Van Hoof, H., Dudek, G. (2020). Policy Search on Aggregated State Space for Active Sampling. In: Xiao, J., Kröger, T., Khatib, O. (eds) Proceedings of the 2018 International Symposium on Experimental Robotics. ISER 2018. Springer Proceedings in Advanced Robotics, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-33950-0_19

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