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Multi-Stage Monte Carlo Tree Search for Non-Monotone Object Rearrangement Planning in Narrow Confined Environments (2305.17175v3)

Published 26 May 2023 in cs.RO

Abstract: Non-monotone object rearrangement planning in confined spaces such as cabinets and shelves is a widely occurring but challenging problem in robotics. Both the robot motion and the available regions for object relocation are highly constrained because of the limited space. This work proposes a Multi-Stage Monte Carlo Tree Search (MS-MCTS) method to solve non-monotone object rearrangement planning problems in confined spaces. Our approach decouples the complex problem into simpler subproblems using an object stage topology. A subgoal-focused tree expansion algorithm that jointly considers the high-level planning and the low-level robot motion is designed to reduce the search space and better guide the search process. By fitting the task into the MCTS paradigm, our method produces optimistic solutions by balancing exploration and exploitation. The experiments demonstrate that our method outperforms the existing methods in terms of the planning time, the number of steps, and the total move distance. Moreover, we deploy our MS-MCTS to a real-world robot system and verify its performance in different scenarios.

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References (33)
  1. J. Reif and M. Sharir, “Motion planning in the presence of moving obstacles,” Journal of the ACM (JACM), vol. 41, no. 4, pp. 764–790, 1994.
  2. R. Wang, Y. Miao, and K. E. Bekris, “Efficient and high-quality prehensile rearrangement in cluttered and confined spaces,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 1968–1975.
  3. P. C. Chen and Y. K. Hwang, “Practical path planning among movable obstacles,” Sandia National Labs., Albuquerque, NM (USA), Tech. Rep., 1990.
  4. M. Stilman and J. J. Kuffner, “Navigation among movable obstacles: Real-time reasoning in complex environments,” International Journal of Humanoid Robotics, vol. 2, no. 04, pp. 479–503, 2005.
  5. C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-Pérez, “Integrated task and motion planning,” Annual review of control, robotics, and autonomous systems, vol. 4, pp. 265–293, 2021.
  6. S. Srivastava, E. Fang, L. Riano, R. Chitnis, S. Russell, and P. Abbeel, “Combined task and motion planning through an extensible planner-independent interface layer,” in 2014 IEEE international conference on robotics and automation (ICRA).   IEEE, 2014, pp. 639–646.
  7. M. Stilman, K. Nishiwaki, S. Kagami, and J. J. Kuffner, “Planning and executing navigation among movable obstacles,” Advanced Robotics, vol. 21, no. 14, pp. 1617–1634, 2007.
  8. M. Stilman and J. Kuffner, “Planning among movable obstacles with artificial constraints,” The International Journal of Robotics Research, vol. 27, no. 11-12, pp. 1295–1307, 2008.
  9. M. Stilman, J.-U. Schamburek, J. Kuffner, and T. Asfour, “Manipulation planning among movable obstacles,” in Proceedings 2007 IEEE international conference on robotics and automation.   IEEE, 2007, pp. 3327–3332.
  10. A. Krontiris and K. E. Bekris, “Efficiently solving general rearrangement tasks: A fast extension primitive for an incremental sampling-based planner,” in 2016 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2016, pp. 3924–3931.
  11. G. Havur, G. Ozbilgin, E. Erdem, and V. Patoglu, “Geometric rearrangement of multiple movable objects on cluttered surfaces: A hybrid reasoning approach,” in 2014 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2014, pp. 445–452.
  12. K. Gao, D. Lau, B. Huang, K. E. Bekris, and J. Yu, “Fast high-quality tabletop rearrangement in bounded workspace,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 1961–1967.
  13. W. Liu, C. Paxton, T. Hermans, and D. Fox, “Structformer: Learning spatial structure for language-guided semantic rearrangement of novel objects,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 6322–6329.
  14. A. Curtis, X. Fang, L. P. Kaelbling, T. Lozano-Pérez, and C. R. Garrett, “Long-horizon manipulation of unknown objects via task and motion planning with estimated affordances,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 1940–1946.
  15. W. Goodwin, S. Vaze, I. Havoutis, and I. Posner, “Semantically grounded object matching for robust robotic scene rearrangement,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 11 138–11 144.
  16. A. H. Qureshi, A. Mousavian, C. Paxton, M. C. Yip, and D. Fox, “Nerp: Neural rearrangement planning for unknown objects,” arXiv preprint arXiv:2106.01352, 2021.
  17. A. Zeng, P. Florence, J. Tompson, S. Welker, J. Chien, M. Attarian, T. Armstrong, I. Krasin, D. Duong, V. Sindhwani, et al., “Transporter networks: Rearranging the visual world for robotic manipulation,” in Conference on Robot Learning.   PMLR, 2021, pp. 726–747.
  18. W. Yuan, K. Hang, D. Kragic, M. Y. Wang, and J. A. Stork, “End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer,” Robotics and Autonomous Systems, vol. 119, pp. 119–134, 2019.
  19. W. Yuan, J. A. Stork, D. Kragic, M. Y. Wang, and K. Hang, “Rearrangement with nonprehensile manipulation using deep reinforcement learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2018, pp. 270–277.
  20. A. Goyal, A. Mousavian, C. Paxton, Y.-W. Chao, B. Okorn, J. Deng, and D. Fox, “Ifor: Iterative flow minimization for robotic object rearrangement,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 14 787–14 797.
  21. R. Wang, K. Gao, D. Nakhimovich, J. Yu, and K. E. Bekris, “Uniform object rearrangement: From complete monotone primitives to efficient non-monotone informed search,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 6621–6627.
  22. R. Wang, K. Gao, J. Yu, and K. Bekris, “Lazy rearrangement planning in confined spaces,” in Proceedings of the International Conference on Automated Planning and Scheduling, vol. 32, 2022, pp. 385–393.
  23. J. Lee, C. Nam, J. Park, and C. Kim, “Tree search-based task and motion planning with prehensile and non-prehensile manipulation for obstacle rearrangement in clutter,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 8516–8522.
  24. C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton, “A survey of monte carlo tree search methods,” IEEE Transactions on Computational Intelligence and AI in games, vol. 4, no. 1, pp. 1–43, 2012.
  25. D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, et al., “A general reinforcement learning algorithm that masters chess, shogi, and go through self-play,” Science, vol. 362, no. 6419, pp. 1140–1144, 2018.
  26. M. C. Fu, “Alphago and monte carlo tree search: the simulation optimization perspective,” in 2016 Winter Simulation Conference (WSC).   IEEE, 2016, pp. 659–670.
  27. T. M. Moerland, J. Broekens, A. Plaat, and C. M. Jonker, “A0c: Alpha zero in continuous action space,” arXiv preprint arXiv:1805.09613, 2018.
  28. Y. Labbé, S. Zagoruyko, I. Kalevatykh, I. Laptev, J. Carpentier, M. Aubry, and J. Sivic, “Monte-carlo tree search for efficient visually guided rearrangement planning,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3715–3722, 2020.
  29. H. Song, J. A. Haustein, W. Yuan, K. Hang, M. Y. Wang, D. Kragic, and J. A. Stork, “Multi-object rearrangement with monte carlo tree search: A case study on planar nonprehensile sorting,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 9433–9440.
  30. J. E. King, V. Ranganeni, and S. S. Srinivasa, “Unobservable monte carlo planning for nonprehensile rearrangement tasks,” in 2017 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2017, pp. 4681–4688.
  31. S. K. Chan, “An iterative general inverse kinematics solution with variable damping,” Ph.D. dissertation, University of British Columbia, 1987.
  32. J. Huynh, “Separating axis theorem for oriented bounding boxes,” URL: jkh. me/files/tutorials/Separating% 20Axis% 20Theorem% 20for% 20Oriented% 20Bounding% 20Boxes. pdf, 2009.
  33. A. Krontiris and K. E. Bekris, “Dealing with difficult instances of object rearrangement.” in Robotics: Science and Systems, vol. 1123, 2015.

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