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A Unified Sampling-Based Approach to Integrated Task and Motion Planning

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Robotics Research (ISRR 2019)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 20))

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Abstract

We present a novel method for performing integrated task and motion planning (TMP) by adapting any off-the-shelf sampling-based motion planning algorithm to simultaneously solve for a symbolically and geometrically feasible plan using a single motion planner invocation. The core insight of our technique is an embedding of symbolic state into continuous space, coupled with a novel means of automatically deriving a function guiding a planner to regions of continuous space where symbolic actions can be executed. Our technique makes few assumptions and offers a great degree of flexibility and generality compared to state of the art planners. We describe our technique and offer a proof of probabilistic completeness along with empirical evaluation of our technique on manipulation benchmark problems.

This material is based upon work supported by the National Science Foundation under Grant No. 1646417 and by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. We are grateful for this support.

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Notes

  1. 1.

    The distinction between facts captured or not captured by the configuration space is largely a choice of granularity in representation.

  2. 2.

    Although we restrict ourselves to this sufficient set of operators in this paper, \(\epsilon \)-weakening can be extended to allow a broader operator set.

  3. 3.

    For deterministic, stateless \(\mathbb {H}\), the set of actions returned will be the same on every invocation. If \(\mathbb {H}\) tracks the set of actions already suggested or is nondeterministic, then the set of actions returned may differ on subsequent invocations of \(\mathbb {H}\) for q.

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Authors and Affiliations

  1. Department of Computer Science, Cornell University, Ithaca, NY, 14850, USA

    Wil Thomason & Ross A. Knepper

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  1. Wil Thomason
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  2. Ross A. Knepper
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Corresponding author

Correspondence to Wil Thomason .

Editor information

Editors and Affiliations

  1. Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Baden-Württemberg, Germany

    Tamim Asfour

  2. Department of Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan

    Eiichi Yoshida

  3. Seoul National University, Seoul, Korea (Republic of)

    Jaeheung Park

  4. Jacobs School of Engineering, Institute for Contextual Robotics, San Diego, CA, USA

    Henrik Christensen

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

    Oussama Khatib

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Thomason, W., Knepper, R.A. (2022). A Unified Sampling-Based Approach to Integrated Task and Motion Planning. In: Asfour, T., Yoshida, E., Park, J., Christensen, H., Khatib, O. (eds) Robotics Research. ISRR 2019. Springer Proceedings in Advanced Robotics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-030-95459-8_47

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