Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Model-based Manipulation of Deformable Objects with Non-negligible Dynamics as Shape Regulation (2402.16114v1)

Published 25 Feb 2024 in cs.RO

Abstract: Model-based manipulation of deformable objects has traditionally dealt with objects in the quasi-static regimes, either because they are extremely lightweight/small or constrained to move very slowly. On the contrary, soft robotic research has made considerable strides toward general modeling and control - despite soft robots and deformable linear objects being very similar from a mechanical standpoint. In this work, we leverage these recent results to develop a fully dynamic framework of slender deformable objects grasped at one of their ends by a robotic manipulator. We introduce a dynamic model of this system using functional strain parameterizations and describe the manipulation challenge as a regulation control problem. This enables us to define a fully model-based control architecture, for which we can prove analytically closed-loop stability and provide sufficient conditions for steady state convergence to the desired manipulation state. The nature of this work is intended to be markedly experimental. We propose an extensive experimental validation of the proposed ideas. For that, we use a 7-DoF robot tasked with the goal of positioning the distal end of six different electric cables, moving on a plane, in a given position and orientation in space.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (60)
  1. V. E. Arriola-Rios, P. Guler, F. Ficuciello, D. Kragic, B. Siciliano, and J. L. Wyatt, “Modeling of deformable objects for robotic manipulation: A tutorial and review,” Frontiers in Robotics and AI, vol. 7, p. 82, 2020.
  2. H. Yin, A. Varava, and D. Kragic, “Modeling, learning, perception, and control methods for deformable object manipulation,” Science Robotics, vol. 6, no. 54, p. eabd8803, 2021.
  3. J. Zhu, A. Cherubini, C. Dune, D. Navarro-Alarcon, F. Alambeigi, D. Berenson, F. Ficuciello, K. Harada, J. Kober, X. Li et al., “Challenges and outlook in robotic manipulation of deformable objects,” IEEE Robotics & Automation Magazine, vol. 29, no. 3, pp. 67–77, 2022.
  4. P. Fiorini, K. Y. Goldberg, Y. Liu, and R. H. Taylor, “Concepts and trends in autonomy for robot-assisted surgery,” Proceedings of the IEEE, vol. 110, no. 7, pp. 993–1011, 2022.
  5. “Robot hands and the mechanics of manipulation,” 1985.
  6. A. Bicchi, “On the problem of decomposing grasp and manipulation forces in multiple whole-limb manipulation,” Robotics and Autonomous Systems, vol. 13, no. 2, pp. 127–147, 1994.
  7. N. C. Dafle, A. Rodriguez, R. Paolini, B. Tang, S. S. Srinivasa, M. Erdmann, M. T. Mason, I. Lundberg, H. Staab, and T. Fuhlbrigge, “Extrinsic dexterity: In-hand manipulation with external forces,” in 2014 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2014, pp. 1578–1585.
  8. T. Pang, H. T. Suh, L. Yang, and R. Tedrake, “Global planning for contact-rich manipulation via local smoothing of quasi-dynamic contact models,” IEEE Transactions on Robotics, 2023.
  9. D. Navarro-Alarcon, Y.-H. Liu, J. G. Romero, and P. Li, “Model-free visually servoed deformation control of elastic objects by robot manipulators,” IEEE Transactions on Robotics, vol. 29, no. 6, pp. 1457–1468, 2013.
  10. 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).   IEEE, 2018, pp. 479–484.
  11. R. Lagneau, A. Krupa, and M. Marchal, “Active deformation through visual servoing of soft objects,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 8978–8984.
  12. J. Zhu, D. Navarro-Alarcon, R. Passama, and A. Cherubini, “Vision-based manipulation of deformable and rigid objects using subspace projections of 2d contours,” Robotics and Autonomous Systems, vol. 142, p. 103798, 2021.
  13. M. Shetab-Bushehri, M. Aranda, Y. Mezouar, and E. Özgür, “As-rigid-as-possible shape servoing,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3898–3905, 2022.
  14. M. Yu, H. Zhong, and X. Li, “Shape control of deformable linear objects with offline and online learning of local linear deformation models,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 1337–1343.
  15. V. Lim, H. Huang, L. Y. Chen, J. Wang, J. Ichnowski, D. Seita, M. Laskey, and K. Goldberg, “Planar Robot Casting with Real2Sim2Real Self-Supervised Learning,” Jun. 2022, arXiv:2111.04814 [cs]. [Online]. Available: http://arxiv.org/abs/2111.04814
  16. C. Chi, B. Burchfiel, E. Cousineau, S. Feng, and S. Song, “Iterative Residual Policy: for Goal-Conditioned Dynamic Manipulation of Deformable Objects,” Apr. 2022, arXiv:2203.00663 [cs]. [Online]. Available: http://arxiv.org/abs/2203.00663
  17. H. Zhang, J. Ichnowski, D. Seita, J. Wang, H. Huang, and K. Goldberg, “Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), May 2021, pp. 4560–4567, iSSN: 2577-087X.
  18. X. Lin, Y. Wang, J. Olkin, and D. Held, “Softgym: Benchmarking deep reinforcement learning for deformable object manipulation,” in Conference on Robot Learning.   PMLR, 2021, pp. 432–448.
  19. F. Liu, E. Su, J. Lu, M. Li, and M. C. Yip, “Robotic manipulation of deformable rope-like objects using differentiable compliant position-based dynamics,” IEEE Robotics and Automation Letters, 2023.
  20. A. Caporali, M. Pantano, L. Janisch, D. Regulin, G. Palli, and D. Lee, “A weakly supervised semi-automatic image labeling approach for deformable linear objects,” IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 1013–1020, 2023.
  21. S. Kuroki, J. Guo, T. Matsushima, T. Okubo, M. Kobayashi, Y. Ikeda, R. Takanami, P. Yoo, Y. Matsuo, and Y. Iwasawa, “Generalizable one-shot rope manipulation with parameter-aware policy,” arXiv preprint arXiv:2306.09872, 2023.
  22. A. Caporali, P. Kicki, K. Galassi, R. Zanella, K. Walas, and G. Palli, “Deformable linear objects manipulation with online model parameters estimation,” IEEE Robotics and Automation Letters, 2024.
  23. “Port-based modeling of a flexible link,” IEEE transactions on robotics, vol. 23, no. 4, pp. 650–660, 2007.
  24. M. Tognon, C. Gabellieri, L. Pallottino, and A. Franchi, “Cooperative aerial transportation without communication: the role of internal force for pose regulation.”
  25. K. D. Do, “Stabilization of exact nonlinear timoshenko beams in space by boundary feedback,” Journal of Sound and Vibration, vol. 422, pp. 278–299, 2018.
  26. A. Mattioni, Y. Wu, H. Ramirez, Y. Le Gorrec, and A. Macchelli, “Modelling and control of an ipmc actuated flexible structure: A lumped port hamiltonian approach,” Control Engineering Practice, vol. 101, p. 104498, 2020.
  27. C. Gabellieri and A. Franchi, “Differential flatness and manipulation of elasto-flexible cables carried by aerial robots in a possibly viscous environment,” in 2023 International Conference on Unmanned Aircraft Systems (ICUAS).   IEEE, 2023, pp. 963–968.
  28. C. Della Santina, C. Duriez, and D. Rus, “Model-based control of soft robots: A survey of the state of the art and open challenges,” IEEE Control Systems Magazine, vol. 43, no. 3, pp. 30–65, 2023.
  29. D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” Nature, vol. 521, no. 7553, pp. 467–475, 2015.
  30. C. Della Santina, M. G. Catalano, and A. Bicchi, “Soft robots,” Encyclopedia of Robotics, 2021.
  31. C. Armanini, F. Boyer, A. T. Mathew, C. Duriez, and F. Renda, “Soft Robots Modeling: A Structured Overview,” IEEE Transactions on Robotics, pp. 1–21, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/10008950/
  32. B. J. Caasenbrood, A. Y. Pogromsky, and H. Nijmeijer, “Sorotoki: A matlab toolkit for design, modeling, and control of soft robots,” IEEE Access, 2024.
  33. D. Navarro-Alarcon and Y.-H. Liu, “Fourier-based shape servoing: a new feedback method to actively deform soft objects into desired 2-d image contours,” IEEE Transactions on Robotics, vol. 34, no. 1, pp. 272–279, 2017.
  34. J. Qi, W. Ma, D. Navarro-Alarcon, H. Gao, and G. Ma, “Adaptive shape servoing of elastic rods using parameterized regression features and auto-tuning motion controls,” arXiv preprint arXiv:2008.06896, 2020.
  35. G. Palli, “Model-based manipulation of deformable linear objects by multivariate dynamic splines,” in 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), vol. 1.   IEEE, 2020, pp. 520–525.
  36. S. H. Sadati, S. E. Naghibi, I. D. Walker, K. Althoefer, and T. Nanayakkara, “Control space reduction and real-time accurate modeling of continuum manipulators using ritz and ritz–galerkin methods,” IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 328–335, 2017.
  37. Z. Wu, M. D. I. Reyzabal, S. H. Sadati, H. Liu, S. Ourselin, D. Leff, R. K. Katzschmann, K. Rhode, and C. Bergeles, “Towards a physics-based model for steerable eversion growing robots,” IEEE robotics and automation letters, vol. 8, no. 2, pp. 1005–1012, 2023.
  38. S. Grazioso, G. Di Gironimo, and B. Siciliano, “A geometrically exact model for soft continuum robots: The finite element deformation space formulation,” Soft robotics, vol. 6, no. 6, pp. 790–811, 2019.
  39. F. Renda, C. Armanini, V. Lebastard, F. Candelier, and F. Boyer, “A geometric variable-strain approach for static modeling of soft manipulators with tendon and fluidic actuation,” IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4006–4013, 2020.
  40. C. D. Santina, “The Soft Inverted Pendulum with Affine Curvature,” in 2020 59th IEEE Conference on Decision and Control (CDC).   Jeju, Korea (South): IEEE, Dec. 2020, pp. 4135–4142. [Online]. Available: https://ieeexplore.ieee.org/document/9303976/
  41. F. Stella, N. Obayashi, C. Della Santina, and J. Hughes, “An Experimental Validation of the Polynomial Curvature Model: Identification and Optimal Control of a Soft Underwater Tentacle,” IEEE Robotics and Automation Letters, vol. 7, pp. 1–8, Oct. 2022.
  42. T. George Thuruthel, Y. Ansari, E. Falotico, and C. Laschi, “Control strategies for soft robotic manipulators: A survey,” Soft robotics, vol. 5, no. 2, pp. 149–163, 2018.
  43. H.-S. Chang, U. Halder, C.-H. Shih, A. Tekinalp, T. Parthasarathy, E. Gribkova, G. Chowdhary, R. Gillette, M. Gazzola, and P. G. Mehta, “Energy shaping control of a cyberoctopus soft arm,” in 2020 59th IEEE Conference on Decision and Control (CDC).   IEEE, 2020, pp. 3913–3920.
  44. E. Franco, A. Garriga-Casanovas, J. Tang, F. R. y Baena, and A. Astolfi, “Adaptive energy shaping control of a class of nonlinear soft continuum manipulators,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 1, pp. 280–291, 2021.
  45. Z. J. Patterson, A. P. Sabelhaus, and C. Majidi, “Robust control of a multi-axis shape memory alloy-driven soft manipulator,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2210–2217, 2022.
  46. P. Borja, A. Dabiri, and C. Della Santina, “Energy-based shape regulation of soft robots with unactuated dynamics dominated by elasticity,” in 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft).   IEEE, 2022, pp. 396–402.
  47. F. Renda, C. Armanini, A. Mathew, and F. Boyer, “Geometrically-exact inverse kinematic control of soft manipulators with general threadlike actuators’ routing,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7311–7318, 2022.
  48. B. Caasenbrood, A. Pogromsky, and H. Nijmeijer, “Energy-shaping controllers for soft robot manipulators through port-hamiltonian cosserat models,” SN Computer Science, vol. 3, no. 6, p. 494, 2022.
  49. F. Stroppa, M. Selvaggio, N. Agharese, M. Luo, L. H. Blumenschein, E. W. Hawkes, and A. M. Okamura, “Shared-control teleoperation paradigms on a soft-growing robot manipulator,” Journal of Intelligent & Robotic Systems, vol. 109, no. 2, p. 30, 2023.
  50. Z. Wang, G. Wang, X. Chen, and N. M. Freris, “Dynamic modeling and control of a soft robotic arm using a piecewise universal joint model,” in 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO).   IEEE, 2023, pp. 1–6.
  51. F. Renda, A. Mathew, and D. F. Talegon, “Dynamics and control of soft robots with implicit strain parametrization,” IEEE Robotics and Automation Letters, 2024.
  52. L. Besselaar and C. D. Santina, “One-shot Learning Closed-loop Manipulation of Soft Slender Objects Based on a Planar Polynomial Curvature Model,” in 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), Apr. 2022, pp. 518–524.
  53. C. Della Santina and D. Rus, “Control oriented modeling of soft robots: the polynomial curvature case,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 290–298, 2019.
  54. F. Stella, Q. Guan, J. Leng, C. Della Santina, and J. Hughes, “Piecewise Affine Curvature model: a reduced-order model for soft robot-environment interaction beyond PCC,” Nov. 2022, arXiv:2211.10188 [cs, eess]. [Online]. Available: http://arxiv.org/abs/2211.10188
  55. T. Baaij, M. K. Holkenborg, M. Stölzle, D. van der Tuin, J. Naaktgeboren, R. Babuška, and C. Della Santina, “Learning 3d shape proprioception for continuum soft robots with multiple magnetic sensors,” Soft Matter, vol. 19, no. 1, pp. 44–56, 2023.
  56. H. Altenbach, M. Bîrsan, and V. A. Eremeyev, “Cosserat-type rods,” Generalized Continua from the Theory to Engineering Applications, pp. 179–248, 2013.
  57. C. D. Santina and D. Rus, “Control Oriented Modeling of Soft Robots: The Polynomial Curvature Case,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 290–298, Apr. 2020, conference Name: IEEE Robotics and Automation Letters.
  58. P. Pustina, C. Della Santina, and A. De Luca, “Feedback regulation of elastically decoupled underactuated soft robots,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4512–4519, 2022.
  59. C. Della Santina, “The soft inverted pendulum with affine curvature,” in 2020 59th IEEE Conference on Decision and Control (CDC).   IEEE, 2020, pp. 4135–4142.
  60. M. Trumic, C. D. Santina, K. Jovanovic, and A. Fagiolini, “On the Stability of the Soft Pendulum With Affine Curvature: Open-Loop, Collocated Closed-Loop, and Switching Control,” IEEE Control Systems Letters, vol. 7, pp. 385–390, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/9811102/
Citations (1)

Summary

We haven't generated a summary for this paper yet.