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      RHH-LGP: Receding horizon and heuristics-based logic-geometric programming for task and motion planning

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      Author(s)
      Braun, C. V.
      Ortiz-Haro, J.
      Toussaint, M.
      Oğuz, Özgür S.
      Date
      2022
      Source Title
      2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
      Print ISSN
      2153-0858
      Electronic ISSN
      2153-0866
      Publisher
      IEEE
      Pages
      13761 - 13768
      Language
      English
      Type
      Conference Paper
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      Abstract
      Sequential decision-making and motion planning for robotic manipulation induce combinatorial complexity. For long-horizon tasks, especially when the environment comprises many objects that can be interacted with, planning efficiency becomes even more important. To plan such long-horizon tasks, we present the RHH-LGP algorithm for combined task and motion planning (TAMP). First, we propose a TAMP approach (based on Logic-Geometric Programming) that effectively uses geometry-based heuristics for solving long-horizon manipulation tasks. The efficiency of this planner is then further improved by a receding horizon formulation, resulting in RHH-LGP. We demonstrate the robustness and effectiveness of our approach on a diverse range of long-horizon tasks that require reasoning about interactions with a large number of objects. Using our framework, we can solve tasks that require multiple robots, including a mobile robot and snake-like walking robots, to form novel heterogeneous kinematic structures autonomously. By combining geometry-based heuristics with iterative planning, our approach brings an order-of-magnitude reduction of planning time in all investigated problems.
      Permalink
      http://hdl.handle.net/11693/111675
      Published Version (Please cite this version)
      https://dx.doi.org/10.1109/IROS47612.2022.9981797
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