Braun, C. V.Ortiz-Haro, J.Toussaint, M.Oğuz, Özgür S.2023-02-242023-02-242022978-1-6654-7928-82153-0858http://hdl.handle.net/11693/111675Conference Name: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Date of Conference: 23-27 October 2022Sequential 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.EnglishConference Paper10.1109/IROS47612.2022.9981797978-1-6654-7927-12153-0866