Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 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

Data-driven Methods Applied to Soft Robot Modeling and Control: A Review (2305.12137v3)

Published 20 May 2023 in cs.RO

Abstract: Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (138)
  1. M. Cianchetti, T. Ranzani, G. Gerboni, I. De Falco, C. Laschi, and A. Menciassi, “Stiff-flop surgical manipulator: Mechanical design and experimental characterization of the single module,” in 2013 IEEE/RSJ international conference on intelligent robots and systems.   IEEE, 2013, pp. 3576–3581.
  2. Y. Ansari, M. Manti, E. Falotico, M. Cianchetti, and C. Laschi, “Multiobjective optimization for stiffness and position control in a soft robot arm module,” IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 108–115, 2018.
  3. J. F. Queißer, K. Neumann, M. Rolf, R. F. Reinhart, and J. J. Steil, “An active compliant control mode for interaction with a pneumatic soft robot,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2014, pp. 573–579.
  4. Z. Q. Tang, H. L. Heung, X. Q. Shi, K. Y. Tong, and Z. Li, “Probabilistic model-based learning control of a soft pneumatic glove for hand rehabilitation,” IEEE Transactions on Biomedical Engineering, vol. 69, no. 2, pp. 1016–1028, 2021.
  5. J. Tolvanen, J. Hannu, and H. Jantunen, “Stretchable and washable strain sensor based on cracking structure for human motion monitoring,” Scientific Reports, vol. 8, no. 1, p. 13241, 2018.
  6. C. Laschi, M. Cianchetti, B. Mazzolai, L. Margheri, M. Follador, and P. Dario, “Soft robot arm inspired by the octopus,” Advanced robotics, vol. 26, no. 7, pp. 709–727, 2012.
  7. S. Joe, M. Totaro, H. Wang, and L. Beccai, “Development of the ultralight hybrid pneumatic artificial muscle: Modelling and optimization,” PloS one, vol. 16, no. 4, p. e0250325, 2021.
  8. A. Menciassi, S. Gorini, G. Pernorio, L. Weiting, F. Valvo, and P. Dario, “Design, fabrication and performances of a biomimetic robotic earthworm,” in 2004 IEEE International Conference on Robotics and Biomimetics, 2004, pp. 274–278.
  9. R. K. Katzschmann, J. DelPreto, R. MacCurdy, and D. Rus, “Exploration of underwater life with an acoustically controlled soft robotic fish,” Science Robotics, vol. 3, no. 16, p. eaar3449, 2018.
  10. J. Sun, L. Bauman, L. Yu, and B. Zhao, “Gecko-and-inchworm-inspired untethered soft robot for climbing on walls and ceilings,” Cell Reports Physical Science, p. 101241, 2023.
  11. K. C. Galloway, K. P. Becker, B. Phillips, J. Kirby, S. Licht, D. Tchernov, R. J. Wood, and D. F. Gruber, “Soft robotic grippers for biological sampling on deep reefs,” Soft robotics, vol. 3, no. 1, pp. 23–33, 2016.
  12. C. Firth, K. Dunn, M. H. Haeusler, and Y. Sun, “Anthropomorphic soft robotic end-effector for use with collaborative robots in the construction industry,” Automation in Construction, vol. 138, p. 104218, 2022.
  13. K. Chin, T. Hellebrekers, and C. Majidi, “Machine learning for soft robotic sensing and control,” Advanced Intelligent Systems, vol. 2, no. 6, p. 1900171, 2020.
  14. J. Wang and A. Chortos, “Control strategies for soft robot systems,” Advanced Intelligent Systems, vol. 4, no. 5, p. 2100165, 2022.
  15. Z. Q. Tang, H. L. Heung, K. Y. Tong, and Z. Li, “A probabilistic model-based online learning optimal control algorithm for soft pneumatic actuators,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1437–1444, 2020.
  16. K.-H. Lee, D. K. Fu, M. C. Leong, M. Chow, H.-C. Fu, K. Althoefer, K. Y. Sze, C.-K. Yeung, and K.-W. Kwok, “Nonparametric online learning control for soft continuum robot: An enabling technique for effective endoscopic navigation,” Soft robotics, vol. 4, no. 4, pp. 324–337, 2017.
  17. C. Duriez, “Control of elastic soft robots based on real-time finite element method,” in 2013 IEEE international conference on robotics and automation.   IEEE, 2013, pp. 3982–3987.
  18. P. E. Dupont, J. Lock, B. Itkowitz, and E. Butler, “Design and control of concentric-tube robots,” IEEE Transactions on Robotics, vol. 26, no. 2, pp. 209–225, 2009.
  19. T. G. Thuruthel, E. Falotico, M. Manti, and C. Laschi, “Stable open loop control of soft robotic manipulators,” IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 1292–1298, 2018.
  20. M. C. Yip and D. B. Camarillo, “Model-less hybrid position/force control: a minimalist approach for continuum manipulators in unknown, constrained environments,” IEEE Robotics and Automation Letters, vol. 1, no. 2, pp. 844–851, 2016.
  21. H. Wang, J. Chen, H. Y. Lau, and H. Ren, “Motion planning based on learning from demonstration for multiple-segment flexible soft robots actuated by electroactive polymers,” IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 391–398, 2016.
  22. C. Cheng, J. Cheng, and W. Huang, “Design and development of a novel sma actuated multi-dof soft robot,” IEEE Access, vol. 7, pp. 75 073–75 080, 2019.
  23. T. G. Thuruthel, E. Falotico, F. Renda, and C. Laschi, “Model-based reinforcement learning for closed-loop dynamic control of soft robotic manipulators,” IEEE Transactions on Robotics, vol. 35, no. 1, pp. 124–134, 2018.
  24. X. T. Ha, D. Wu, M. Ourak, G. Borghesan, J. Dankelman, A. Menciassi, and E. V. Poorten, “Shape sensing of flexible robots based on deep learning,” IEEE Transactions on Robotics, pp. 1–14, 2022.
  25. J. Jung, M. Park, D. Kim, and Y.-L. Park, “Optically sensorized elastomer air chamber for proprioceptive sensing of soft pneumatic actuators,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2333–2340, 2020.
  26. X. T. Ha, D. Wu, C.-F. Lai, M. Ourak, G. Borghesan, A. Menciassi, and E. Vander Poorten, “Contact localization of continuum and flexible robot using data-driven approach,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6910–6917, 2022.
  27. Z. Dong, X. Wang, G. Fang, Z. He, J. D.-L. Ho, C.-L. Cheung, W. L. Tang, X. Xie, L. Liang, H.-C. Chang et al., “Shape tracking and feedback control of cardiac catheter using mri-guided robotic platform—validation with pulmonary vein isolation simulator in mri,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 2781–2798, 2022.
  28. C. Lee, M. Kim, Y. J. Kim, N. Hong, S. Ryu, H. J. Kim, and S. Kim, “Soft robot review,” International Journal of Control, Automation and Systems, vol. 15, pp. 3–15, 2017.
  29. K. Liu, W. Chen, W. Yang, Z. Jiao, and Y. Yu, “Review of the research progress in soft robots,” Applied Sciences, vol. 13, no. 1, p. 120, 2022.
  30. J. M. Bern, Y. Schnider, P. Banzet, N. Kumar, and S. Coros, “Soft robot control with a learned differentiable model,” in 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft).   IEEE, 2020, pp. 417–423.
  31. K. Iyengar, S. Spurgeon, and D. Stoyanov, “Deep reinforcement learning for concentric tube robot path following,” IEEE Transactions on Medical Robotics and Bionics, 2023.
  32. D. Wu, X. T. Ha, Y. Zhang, M. Ourak, G. Borghesan, K. Niu, F. Trauzettel, J. Dankelman, A. Menciassi, and E. Vander Poorten, “Deep-learning-based compliant motion control of a pneumatically-driven robotic catheter,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 8853–8860, 2022.
  33. A. T. Tibebu, B. Yu, Y. Kassahun, E. Vander Poorten, and P. T. Tran, “Towards autonomous robotic catheter navigation using reinforcement learning,” 4th Joint Workshop on New Technologies for Computer/Robot Assisted Surgery, 2014.
  34. M. C. Yip and D. B. Camarillo, “Model-less feedback control of continuum manipulators in constrained environments,” IEEE Transactions on Robotics, vol. 30, no. 4, pp. 880–889, 2014.
  35. A. Melingui, C. Escande, N. Benoudjit, R. Merzouki, and J. B. Mbede, “Qualitative approach for forward kinematic modeling of a compact bionic handling assistant trunk,” IFAC Proceedings Volumes, vol. 47, no. 3, pp. 9353–9358, 2014.
  36. M. T. Gillespie, C. M. Best, E. C. Townsend, D. Wingate, and M. D. Killpack, “Learning nonlinear dynamic models of soft robots for model predictive control with neural networks,” in 2018 IEEE International Conference on Soft Robotics (RoboSoft).   IEEE, 2018, pp. 39–45.
  37. S. Satheeshbabu, N. K. Uppalapati, G. Chowdhary, and G. Krishnan, “Open loop position control of soft continuum arm using deep reinforcement learning,” in 2019 International Conference on Robotics and Automation (ICRA).   IEEE, 2019, pp. 5133–5139.
  38. A. Centurelli, L. Arleo, A. Rizzo, S. Tolu, C. Laschi, and E. Falotico, “Closed-loop dynamic control of a soft manipulator using deep reinforcement learning,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4741–4748, 2022.
  39. M. Li, R. Kang, D. T. Branson, and J. S. Dai, “Model-free control for continuum robots based on an adaptive kalman filter,” IEEE/ASME Transactions on Mechatronics, vol. 23, no. 1, pp. 286–297, 2018.
  40. X. Wang, J. Dai, H.-S. Tong, K. Wang, G. Fang, X. Xie, Y.-H. Liu, K. W. S. Au, and K.-W. Kwok, “Learning-based visual-strain fusion for eye-in-hand continuum robot pose estimation and control,” IEEE Transactions on Robotics, 2023.
  41. 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.
  42. 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.
  43. C. Laschi, T. G. Thuruthel, F. Lida, R. Merzouki, and E. Falotico, “Learning-based control strategies for soft robots: Theory, achievements, and future challenges,” IEEE Control Systems Magazine, vol. 43, no. 3, pp. 100–113, 2023.
  44. B. Zhang and P. Liu, “Model-based and model-free robot control: a review,” in RiTA 2020: Proceedings of the 8th International Conference on Robot Intelligence Technology and Applications.   Springer, 2021, pp. 45–55.
  45. C. Schlagenhauf, D. Bauer, K.-H. Chang, J. P. King, D. Moro, S. Coros, and N. Pollard, “Control of tendon-driven soft foam robot hands,” in 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids).   IEEE, 2018, pp. 1–7.
  46. G. Runge, M. Wiese, and A. Raatz, “Fem-based training of artificial neural networks for modular soft robots,” in 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).   IEEE, 2017, pp. 385–392.
  47. M. Wiese, G. Runge-Borchert, and A. Raatz, “Optimization of neural network hyperparameters for modeling of soft pneumatic actuators,” in New Trends in Medical and Service Robotics: Advances in Theory and Practice.   Springer, 2019, pp. 199–206.
  48. F. Faure, C. Duriez, H. Delingette, J. Allard, B. Gilles, S. Marchesseau, H. Talbot, H. Courtecuisse, G. Bousquet, I. Peterlik, and S. Cotin, “SOFA: A Multi-Model Framework for Interactive Physical Simulation,” in Soft Tissue Biomechanical Modeling for Computer Assisted Surgery, ser. Studies in Mechanobiology, Tissue Engineering and Biomaterials, Y. Payan, Ed.   Springer, Jun. 2012, vol. 11, pp. 283–321. [Online]. Available: https://inria.hal.science/hal-00681539
  49. F. Largilliere, V. Verona, E. Coevoet, M. Sanz-Lopez, J. Dequidt, and C. Duriez, “Real-time control of soft-robots using asynchronous finite element modeling,” in 2015 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2015, pp. 2550–2555.
  50. M. Thieffry, A. Kruszewski, C. Duriez, and T.-M. Guerra, “Control design for soft robots based on reduced-order model,” IEEE Robotics and Automation Letters, vol. 4, no. 1, pp. 25–32, 2018.
  51. M. Gazzola, L. Dudte, A. McCormick, and L. Mahadevan, “Forward and inverse problems in the mechanics of soft filaments,” Royal Society open science, vol. 5, no. 6, p. 171628, 2018. [Online]. Available: https://doi.org/10.1098/rsos.171628
  52. 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.
  53. A. T. Mathew, I. M. B. Hmida, C. Armanini, F. Boyer, and F. Renda, “Sorosim: A matlab toolbox for hybrid rigid-soft robots based on the geometric variable-strain approach,” IEEE Robotics & Automation Magazine, 2022.
  54. H. Jiang, Z. Wang, X. Liu, X. Chen, Y. Jin, X. You, and X. Chen, “A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior,” in 2017 IEEE international conference on robotics and automation (ICRA).   IEEE, 2017, pp. 6127–6133.
  55. I. S. Godage, G. A. Medrano-Cerda, D. T. Branson, E. Guglielmino, and D. G. Caldwell, “Dynamics for variable length multisection continuum arms,” The International Journal of Robotics Research, vol. 35, no. 6, pp. 695–722, 2016.
  56. E. Milana, F. Stella, B. Gorissen, D. Reynaerts, and C. Della Santina, “Model-based control can improve the performance of artificial cilia,” in 2021 IEEE 4th International Conference on Soft Robotics (RoboSoft).   IEEE, 2021, pp. 527–530.
  57. M. H. Namdar Ghalati, H. Ghafarirad, A. A. Suratgar, M. Zareinejad, and M. A. Ahmadi-Pajouh, “Static modeling of soft reinforced bending actuator considering external force constraints,” Soft Robotics, vol. 9, no. 4, pp. 776–787, 2022.
  58. R. Grassmann, V. Modes, and J. Burgner-Kahrs, “Learning the forward and inverse kinematics of a 6-dof concentric tube continuum robot in se (3),” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 5125–5132.
  59. C. Della Santina, R. K. Katzschmann, A. Biechi, and D. Rus, “Dynamic control of soft robots interacting with the environment,” in 2018 IEEE International Conference on Soft Robotics (RoboSoft).   IEEE, 2018, pp. 46–53.
  60. Z. Q. Tang, H. L. Heung, K. Y. Tong, and Z. Li, “A novel iterative learning model predictive control method for soft bending actuators,” in 2019 International Conference on Robotics and Automation (ICRA).   IEEE, 2019, pp. 4004–4010.
  61. Z. Wang and S. Hirai, “Soft gripper dynamics using a line-segment model with an optimization-based parameter identification method,” IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 624–631, 2017.
  62. X. Wang, K.-H. Lee, D. K. Fu, Z. Dong, K. Wang, G. Fang, S.-L. Lee, A. P. Lee, and K.-W. Kwok, “Experimental validation of robot-assisted cardiovascular catheterization: model-based versus model-free control,” International journal of computer assisted radiology and surgery, vol. 13, pp. 797–804, 2018.
  63. X. Wang, G. Fang, K. Wang, X. Xie, K.-H. Lee, J. D. Ho, W. L. Tang, J. Lam, and K.-W. Kwok, “Eye-in-hand visual servoing enhanced with sparse strain measurement for soft continuum robots,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2161–2168, 2020.
  64. Y.-Y. Wu and N. Tan, “Model-less feedback control for soft manipulators with jacobian adaptation,” in 2020 International Symposium on Autonomous Systems (ISAS).   IEEE, 2020, pp. 217–222.
  65. M. C. Yip, J. A. Sganga, and D. B. Camarillo, “Autonomous control of continuum robot manipulators for complex cardiac ablation tasks,” Journal of Medical Robotics Research, vol. 2, no. 01, p. 1750002, 2017.
  66. M. Verghese, F. Richter, A. Gunn, P. Weissbrod, and M. Yip, “Model-free visual control for continuum robot manipulators via orientation adaptation,” in The International Symposium of Robotics Research.   Springer, 2019, pp. 959–970.
  67. T. George Thuruthel, E. Falotico, M. Manti, A. Pratesi, M. Cianchetti, and C. Laschi, “Learning closed loop kinematic controllers for continuum manipulators in unstructured environments,” Soft robotics, vol. 4, no. 3, pp. 285–296, 2017.
  68. Y. Jin, Y. Wang, X. Chen, Z. Wang, X. Liu, H. Jiang, and X. Chen, “Model-less feedback control for soft manipulators,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2017, pp. 2916–2922.
  69. H. Jiang, Z. Wang, Y. Jin, X. Chen, P. Li, Y. Gan, S. Lin, and X. Chen, “Hierarchical control of soft manipulators towards unstructured interactions,” The International Journal of Robotics Research, vol. 40, no. 1, pp. 411–434, 2021.
  70. G. Fang, X. Wang, K. Wang, K.-H. Lee, J. D. Ho, H.-C. Fu, D. K. C. Fu, and K.-W. Kwok, “Vision-based online learning kinematic control for soft robots using local gaussian process regression,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1194–1201, 2019.
  71. S. Sefati, R. Hegeman, F. Alambeigi, I. Iordachita, and M. Armand, “Fbg-based position estimation of highly deformable continuum manipulators: Model-dependent vs. data-driven approaches,” in 2019 International Symposium on Medical Robotics (ISMR).   IEEE, 2019, pp. 1–6.
  72. W. Xu, J. Chen, H. Y. Lau, and H. Ren, “Automate surgical tasks for a flexible serpentine manipulator via learning actuation space trajectory from demonstration,” in 2016 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2016, pp. 4406–4413.
  73. I. M. Loutfi, A. B. Boutchouang, A. Melingui, O. Lakhal, F. B. Motto, and R. Merzouki, “Learning-based approaches for forward kinematic modeling of continuum manipulators,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 9899–9904, 2020.
  74. B. Yu, J. d. G. Fernández, and T. Tan, “Probabilistic kinematic model of a robotic catheter for 3d position control,” Soft robotics, vol. 6, no. 2, pp. 184–194, 2019.
  75. A. Melingui, J. J.-B. M. Ahanda, O. Lakhal, J. B. Mbede, and R. Merzouki, “Adaptive algorithms for performance improvement of a class of continuum manipulators,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 9, pp. 1531–1541, 2017.
  76. G. Fagogenis, C. Bergeles, and P. E. Dupont, “Adaptive nonparametric kinematic modeling of concentric tube robots,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2016, pp. 4324–4329.
  77. Z. Tang, P. Wang, W. Xin, and C. Laschi, “Learning-based approach for a soft assistive robotic arm to achieve simultaneous position and force control,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 8315–8322, 2022.
  78. J. D. Ho, K.-H. Lee, W. L. Tang, K.-M. Hui, K. Althoefer, J. Lam, and K.-W. Kwok, “Localized online learning-based control of a soft redundant manipulator under variable loading,” Advanced Robotics, vol. 32, no. 21, pp. 1168–1183, 2018.
  79. M. Malekzadeh, J. Queißer, and J. J. Steil, “Learning the end-effector pose from demonstration for the bionic handling assistant robot,” in 9th International Workshop on Human-Friendly Robotics.   Springer, 2016, pp. 101–107.
  80. S. Calinon, D. Bruno, M. S. Malekzadeh, T. Nanayakkara, and D. G. Caldwell, “Human–robot skills transfer interfaces for a flexible surgical robot,” Computer methods and programs in biomedicine, vol. 116, no. 2, pp. 81–96, 2014.
  81. A. Ataka, P. Qi, A. Shiva, A. Shafti, H. Wurdemann, H. Liu, and K. Althoefer, “Real-time pose estimation and obstacle avoidance for multi-segment continuum manipulator in dynamic environments,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2016, pp. 2827–2832.
  82. C. Jang, J. Ha, P. E. Dupont, and F. C. Park, “Toward on-line parameter estimation of concentric tube robots using a mechanics-based kinematic model,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2016, pp. 2400–2405.
  83. J. Y. Loo, K. C. Kong, C. P. Tan, and S. G. Nurzaman, “Non-linear system identification and state estimation in a pneumatic based soft continuum robot,” in 2019 IEEE Conference on control technology and applications (CCTA).   IEEE, 2019, pp. 39–46.
  84. J. Y. Loo, Z. Y. Ding, E. Davies, S. G. Nurzaman, and C. P. Tan, “Curvature and force estimation for a soft finger using an ekf with unknown input optimization,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 8506–8512, 2020.
  85. D. C. Rucker and R. J. Webster, “Deflection-based force sensing for continuum robots: A probabilistic approach,” in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2011, pp. 3764–3769.
  86. S. Tully, G. Kantor, M. A. Zenati, and H. Choset, “Shape estimation for image-guided surgery with a highly articulated snake robot,” in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2011, pp. 1353–1358.
  87. C. Kim, S. C. Ryu, and P. E. Dupont, “Real-time adaptive kinematic model estimation of concentric tube robots,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2015, pp. 3214–3219.
  88. K. Tan, Q. Ji, L. Feng, and M. Törngren, “Edge-enabled adaptive shape estimation of 3d printed soft actuators with gaussian processes and unscented kalman filters,” IEEE Transactions on Industrial Electronics, 2023.
  89. D. Lunni, G. Giordano, E. Sinibaldi, M. Cianchetti, and B. Mazzolai, “Shape estimation based on kalman filtering: Towards fully soft proprioception,” in 2018 IEEE International Conference on Soft Robotics (RoboSoft).   IEEE, 2018, pp. 541–546.
  90. Z. Tang, P. Wang, W. Xin, Z. Xie, L. Kan, M. Mohanakrishnan, and C. Laschi, “Meta-learning-based optimal control for soft robotic manipulators to interact with unknown environments,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 982–988.
  91. K. Neumann, M. Rolf, and J. J. Steil, “Reliable integration of continuous constraints into extreme learning machines,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 21, no. supp02, pp. 35–50, 2013.
  92. M. Giorelli, F. Renda, G. Ferri, and C. Laschi, “A feed-forward neural network learning the inverse kinetics of a soft cable-driven manipulator moving in three-dimensional space,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 5033–5039.
  93. T. G. Thuruthel, E. Falotico, F. Renda, and C. Laschi, “Learning dynamic models for open loop predictive control of soft robotic manipulators,” Bioinspiration & biomimetics, vol. 12, no. 6, p. 066003, 2017.
  94. G. Soter, A. Conn, H. Hauser, and J. Rossiter, “Bodily aware soft robots: integration of proprioceptive and exteroceptive sensors,” in 2018 IEEE international conference on robotics and automation (ICRA).   IEEE, 2018, pp. 2448–2453.
  95. M. A. Ahmad, C. Gruijthuijsen, M. Ourak, J. Deprest, E. Vander Poorten, and T. Vercauteren, “Shared control of an automatically aligning endoscopic instrument based on convolutional neural networks,” in 9th joint workshop on New Technologies for Computer/Robot Assisted Surgery, Location: Genoa, Italy, 2019.
  96. W. Xu, J. Chen, H. Y. Lau, and H. Ren, “Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators,” The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 13, no. 3, p. e1774, 2017.
  97. R. F. Reinhart and J. J. Steil, “Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot,” Procedia Technology, vol. 26, pp. 12–19, 2016.
  98. R. F. Reinhart, Z. Shareef, and J. J. Steil, “Hybrid analytical and data-driven modeling for feed-forward robot control,” Sensors, vol. 17, no. 2, p. 311, 2017.
  99. D. Braganza, D. M. Dawson, I. D. Walker, and N. Nath, “A neural network controller for continuum robots,” IEEE transactions on robotics, vol. 23, no. 6, pp. 1270–1277, 2007.
  100. M. Giorelli, F. Renda, M. Calisti, A. Arienti, G. Ferri, and C. Laschi, “Neural network and jacobian method for solving the inverse statics of a cable-driven soft arm with nonconstant curvature,” IEEE Transactions on Robotics, vol. 31, no. 4, pp. 823–834, 2015.
  101. 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.
  102. P. Hyatt and M. D. Killpack, “Real-time nonlinear model predictive control of robots using a graphics processing unit,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1468–1475, 2020.
  103. G. Fang, Y. Tian, Z.-X. Yang, J. M. Geraedts, and C. C. Wang, “Efficient jacobian-based inverse kinematics with sim-to-real transfer of soft robots by learning,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 6, pp. 5296–5306, 2022.
  104. A. Kuntz, A. Sethi, R. J. Webster, and R. Alterovitz, “Learning the complete shape of concentric tube robots,” IEEE transactions on medical robotics and bionics, vol. 2, no. 2, pp. 140–147, 2020.
  105. R. Grassmann and J. Burgner-Kahrs, “On the merits of joint space and orientation representations in learning the forward kinematics in se(3),” in Robotics: Science and Systems Conference, 10 pages, 2019. [Online]. Available: http://www.roboticsproceedings.org/rss15/p17.pdf
  106. J. Liu, P. Borja, and C. Della Santina, “Physics-informed neural networks to model and control robots: A theoretical and experimental investigation,” Advanced Intelligent Systems, p. 2300385, 2023.
  107. F. Piqué, H. T. Kalidindi, L. Fruzzetti, C. Laschi, A. Menciassi, and E. Falotico, “Controlling soft robotic arms using continual learning,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5469–5476, 2022.
  108. A. Melingui, O. Lakhal, B. Daachi, J. B. Mbede, and R. Merzouki, “Adaptive neural network control of a compact bionic handling arm,” IEEE/ASME Transactions on Mechatronics, vol. 20, no. 6, pp. 2862–2875, 2015.
  109. X. Li, A. M. H. Tiong, L. Cao, W. Lai, P. T. Phan, and S. J. Phee, “Deep learning for haptic feedback of flexible endoscopic robot without prior knowledge on sheath configuration,” International Journal of Mechanical Sciences, vol. 163, p. 105129, 2019.
  110. S. Yao, R. Tang, L. Bai, H. Yan, H. Ren, and L. Liu, “An rnn-lstm enhanced compact and affordable micro force sensing system for interventional continuum robots with interchangeable end-effector instruments,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–11, 2023.
  111. Z. Y. Ding, J. Y. Loo, V. M. Baskaran, S. G. Nurzaman, and C. P. Tan, “Predictive uncertainty estimation using deep learning for soft robot multimodal sensing,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 951–957, 2021.
  112. T. G. Thuruthel, B. Shih, C. Laschi, and M. T. Tolley, “Soft robot perception using embedded soft sensors and recurrent neural networks,” Science Robotics, vol. 4, no. 26, p. eaav1488, 2019.
  113. Z. Zhou, R. Zuo, B. Ying, J. Zhu, Y. Wang, X. Wang, and X. Liu, “A sensory soft robotic gripper capable of learning-based object recognition and force-controlled grasping,” IEEE Transactions on Automation Science and Engineering, 2022.
  114. Z. Chen, X. Ren, M. Bernabei, V. Mainardi, G. Ciuti, and C. Stefanini, “A hybrid adaptive controller for soft robot interchangeability,” IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 875–882, 2023.
  115. E. Almanzor, F. Ye, J. Shi, T. G. Thuruthel, H. A. Wurdemann, and F. Iida, “Static shape control of soft continuum robots using deep visual inverse kinematic models,” IEEE Transactions on Robotics, 2023.
  116. N. Liang, R. M. Grassmann, S. Lilge, and J. Burgner-Kahrs, “Learning-based inverse kinematics from shape as input for concentric tube continuum robots,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 1387–1393.
  117. U. Yoo, H. Zhao, A. Altamirano, W. Yuan, and C. Feng, “Toward zero-shot sim-to-real transfer learning for pneumatic soft robot 3d proprioceptive sensing,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 544–551.
  118. Y. Zhang, J. Gao, H. Yang, and L. Hao, “A novel hysteresis modelling method with improved generalization capability for pneumatic artificial muscles,” Smart Materials and Structures, vol. 28, no. 10, p. 105014, 2019.
  119. Y. Chen, W. Xu, Z. Li, S. Song, C. M. Lim, Y. Wang, and H. Ren, “Safety-enhanced motion planning for flexible surgical manipulator using neural dynamics,” IEEE Transactions on Control Systems Technology, vol. 25, no. 5, pp. 1711–1723, 2016.
  120. Z. Chen, M. Bernabei, V. Mainardi, X. Ren, G. Ciuti, and C. Stefanini, “A novel and accurate bilstm configuration controller for modular soft robots with module number adaptability,” arXiv preprint arXiv:2401.10997, 2024.
  121. S. Sapai, J. Y. Loo, Z. Y. Ding, C. P. Tan, V. M. Baskaran, and S. G. Nurzaman, “A deep learning framework for soft robots with synthetic data,” Soft robotics, vol. 10, no. 6, pp. 1224–1240, 2023.
  122. A. Melingui, R. Merzouki, J. B. Mbede, C. Escande, and N. Benoudjit, “Neural networks based approach for inverse kinematic modeling of a compact bionic handling assistant trunk,” in 2014 IEEE 23rd international symposium on industrial electronics (ISIE).   IEEE, 2014, pp. 1239–1244.
  123. Y. Engel, P. Szabo, and D. Volkinshtein, “Learning to control an octopus arm with gaussian process temporal difference methods,” Advances in neural information processing systems, vol. 18, 2005.
  124. Y. Kassahun, B. Yu, and E. Vander Poorten, “Learning catheter-aorta interaction model using joint probability densities,” in Proceedings of the 3rd joint workshop on new technologies for computer/robot assisted surgery, 2013, pp. 158–160.
  125. J. Chen, H. Y. Lau, W. Xu, and H. Ren, “Towards transferring skills to flexible surgical robots with programming by demonstration and reinforcement learning,” in 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI).   IEEE, 2016, pp. 378–384.
  126. X. Dong, J. Zhang, L. Cheng, W. Xu, H. Su, and T. Mei, “A policy gradient algorithm integrating long and short-term rewards for soft continuum arm control,” Science China Technological Sciences, vol. 65, no. 10, pp. 2409–2419, 2022.
  127. G. Ji, J. Yan, J. Du, W. Yan, J. Chen, Y. Lu, J. Rojas, and S. S. Cheng, “Towards safe control of continuum manipulator using shielded multiagent reinforcement learning,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7461–7468, 2021.
  128. X. Liu, R. Gasoto, Z. Jiang, C. Onal, and J. Fu, “Learning to locomote with artificial neural-network and cpg-based control in a soft snake robot,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 7758–7765.
  129. X. Liu, C. Onal, and J. Fu, “Learning contact-aware cpg-based locomotion in a soft snake robot,” arXiv preprint arXiv:2105.04608, 2021.
  130. Y. Lu, R. Wei, B. Li, W. Chen, J. Zhou, Q. Dou, D. Sun, and Y.-h. Liu, “Autonomous intelligent navigation for flexible endoscopy using monocular depth guidance and 3-d shape planning,” in 2023 IEEE international conference on robotics and automation (ICRA).   IEEE, 2023, pp. 1–7.
  131. Y. Gan, P. Li, H. Jiang, G. Wang, Y. Jin, X. Chen, and J. Ji, “A reinforcement learning method for motion control with constraints on an hpn arm,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 12 006–12 013, 2022.
  132. O. M. Omisore, T. Akinyemi, W. Duan, W. Du, and L. Wang, “A novel sample-efficient deep reinforcement learning with episodic policy transfer for pid-based control in cardiac catheterization robots,” arXiv preprint arXiv:2110.14941, 2021.
  133. Y. Li, X. Wang, and K.-W. Kwok, “Towards adaptive continuous control of soft robotic manipulator using reinforcement learning,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 7074–7081.
  134. Q. Wu, Y. Gu, Y. Li, B. Zhang, S. A. Chepinskiy, J. Wang, A. A. Zhilenkov, A. Y. Krasnov, and S. Chernyi, “Position control of cable-driven robotic soft arm based on deep reinforcement learning,” Information, vol. 11, no. 6, p. 310, 2020.
  135. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
  136. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
  137. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
  138. M. S. Nazeer, D. Bianchi, G. Campinoti, C. Laschi, and E. Falotico, “Policy adaptation using an online regressing network in a soft robotic arm,” in 2023 IEEE International Conference on Soft Robotics (RoboSoft).   IEEE, 2023, pp. 1–7.
Citations (12)
View on Semantic Scholar

Summary

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

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now