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Sequential Manipulation of Deformable Linear Object Networks with Endpoint Pose Measurements using Adaptive Model Predictive Control (2402.10372v1)

Published 15 Feb 2024 in cs.RO, cs.SY, and eess.SY

Abstract: Robotic manipulation of deformable linear objects (DLOs) is an active area of research, though emerging applications, like automotive wire harness installation, introduce constraints that have not been considered in prior work. Confined workspaces and limited visibility complicate prior assumptions of multi-robot manipulation and direct measurement of DLO configuration (state). This work focuses on single-arm manipulation of stiff DLOs (StDLOs) connected to form a DLO network (DLON), for which the measurements (output) are the endpoint poses of the DLON, which are subject to unknown dynamics during manipulation. To demonstrate feasibility of output-based control without state estimation, direct input-output dynamics are shown to exist by training neural network models on simulated trajectories. Output dynamics are then approximated with polynomials and found to contain well-known rigid body dynamics terms. A composite model consisting of a rigid body model and an online data-driven residual is developed, which predicts output dynamics more accurately than either model alone, and without prior experience with the system. An adaptive model predictive controller is developed with the composite model for DLON manipulation, which completes DLON installation tasks, both in simulation and with a physical automotive wire harness.

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References (26)
  1. M. Saha and P. Isto, “Manipulation Planning for Deformable Linear Objects,” IEEE Transactions on Robotics, vol. 23, pp. 1141–1150, Dec. 2007.
  2. M. Moll and L. Kavraki, “Path planning for deformable linear objects,” IEEE Transactions on Robotics, vol. 22, pp. 625–636, Aug. 2006.
  3. X. Jiang, K.-m. Koo, K. Kikuchi, A. Konmo, and M. Uchiyama, “Robotized Assembly of a Wire Harness in Car Production Line,” in International Conference on Intelligent Robots and Systems, 2010.
  4. N. Lv, J. Liu, and Y. Jia, “Dynamic Modeling and Control of Deformable Linear Objects for Single-Arm and Dual-Arm Robot Manipulations,” IEEE Transactions on Robotics, vol. 38, pp. 2341–2353, Aug. 2022.
  5. C. Wang, Y. Zhang, X. Zhang, Z. Wu, X. Zhu, S. Jin, T. Tang, and M. Tomizuka, “Offline-Online Learning of Deformation Model for Cable Manipulation With Graph Neural Networks,” IEEE Robotics and Automation Letters, vol. 7, pp. 5544–5551, Apr. 2022.
  6. Y. Wu, W. Yan, T. Kurutach, L. Pinto, and P. Abbeel, “Learning to manipulate deformable objects without demonstrations,” in 16th Robotics: Science and Systems, RSS 2020, MIT Press Journals, 2020.
  7. W. Yan, A. Vangipuram, P. Abbeel, and L. Pinto, “Learning predictive representations for deformable objects using contrastive estimation,” in Conference on Robot Learning, pp. 564–574, PMLR, 2021.
  8. H. Han, G. Paul, and T. Matsubara, “Model-based reinforcement learning approach for deformable linear object manipulation,” in 2017 13th IEEE Conference on Automation Science and Engineering (CASE), (Xi’an), pp. 750–755, IEEE, Aug. 2017.
  9. N. Alvarez and K. Yamazaki, “An interactive simulator for deformable linear objects manipulation planning,” in 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), (San Francisco, CA, USA), pp. 259–267, IEEE, Dec. 2016.
  10. P. M. Fresnillo, S. Vasudevan, and W. M. Mohammed, “An approach for the bimanual manipulation of a deformable linear object using a dual-arm industrial robot: cable routing use case,” in 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), (Coventry, United Kingdom), pp. 1–8, IEEE, May 2022.
  11. J. Zhu, B. Navarro, R. Passama, P. Fraisse, A. Crosnier, and A. Cherubini, “Robotic Manipulation Planning for Shaping Deformable Linear Objects With Environmental Contacts,” IEEE Robotics and Automation Letters, vol. 5, pp. 16–23, Jan. 2020.
  12. M. Yu, H. Zhong, F. Zhong, and X. Li, “Adaptive control for robotic manipulation of deformable linear objects with offline and online learning of unknown models,” arXiv e-prints, pp. arXiv–2107, 2021.
  13. V. Lim, H. Huang, L. Y. Chen, J. Wang, J. Ichnowski, D. Seita, M. Laskey, and K. Goldberg, “Real2sim2real: Self-supervised learning of physical single-step dynamic actions for planar robot casting,” in 2022 International Conference on Robotics and Automation (ICRA), pp. 8282–8289, 2022.
  14. H. Zhou, S. Li, Q. Lu, and J. Qian, “A Practical Solution to Deformable Linear Object Manipulation: A Case Study on Cable Harness Connection,” in 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM), (Shenzhen, China), pp. 329–333, IEEE, Dec. 2020.
  15. K. M. Koo, X. Jiang, K. Kikuchi, A. Konno, and M. Uchiyama, “Development of a robot car wiring system,” in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, pp. 862–867, 2008.
  16. J. Zhu, B. Navarro, P. Fraisse, A. Crosnier, and A. Cherubini, “Dual-arm robotic manipulation of flexible cables,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 479–484, IEEE.
  17. T. P. Nguyen and J. Yoon, “A novel vision-based method for 3D profile extraction of wire harness in robotized assembly process,” Journal of Manufacturing Systems, vol. 61, pp. 365–374, oct 2021.
  18. T. Toner, M. Saez, D. M. Tilbury, and K. Barton, “Opportunities and challenges in applying reinforcement learning to robotic manipulation: an industrial case study,” Manufacturing Letters, 2023.
  19. M. Saez and P. Spicer, “Robot-to-robot collaboration for fixtureless assembly: Challenges and opportunities in the automotive industry,” in ASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020, vol. 2, pp. 8–10, 2020.
  20. T. Z. Zhao, J. Luo, O. Sushkov, R. Pevceviciute, N. Heess, J. Scholz, S. Schaal, and S. Levine, “Offline Meta-Reinforcement Learning for Industrial Insertion,” in International Conference on Robotics and Automation (ICRA), 2022.
  21. E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning.” http://pybullet.org, 2016–2019.
  22. T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A Next-generation Hyperparameter Optimization Framework,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2623–2631, 2019.
  23. S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Discovering governing equations from data by sparse identification of nonlinear dynamical systems,” Proceedings of the National Academy of Sciences, vol. 113, pp. 3932–3937, Apr. 2016.
  24. S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Sparse Identification of Nonlinear Dynamics with Control (SINDYc),” IFAC-PapersOnLine, vol. 49, no. 18, pp. 710–715, 2016.
  25. B. de Silva, K. Champion, M. Quade, J.-C. Loiseau, J. Kutz, and S. Brunton, “Pysindy: A python package for the sparse identification of nonlinear dynamical systems from data,” The Journal of Open Source Software, vol. 5, no. 49, p. 2104, 2020.
  26. J. Wang and E. Olson, “AprilTag 2: Efficient and robust fiducial detection,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4193–4198, IEEE, Oct 2016.
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Authors (5)
  1. Tyler Toner (1 paper)
  2. Vahidreza Molazadeh (1 paper)
  3. Miguel Saez (1 paper)
  4. Dawn M. Tilbury (16 papers)
  5. Kira Barton (22 papers)

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