Articles | Volume 17, issue 1
https://doi.org/10.5194/ms-17-313-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ms-17-313-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
25 Mar 2026
Review article |
| 25 Mar 2026
| 25 Mar 2026
Review article: A review of control technologies for soft robots: from structural design to intelligent control
Huijun Yu
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Min Lv
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Bin Hu
Thoracic Surgery Department, Sichuan Cancer Hospital and Institute, Chengdu, China
Yang Zhang
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Cited articles
Alessi, C., Bianchi, D., Stano, G., Cianchetti, M., and Falotico, E.: Pushing with soft robotic arms via deep reinforcement learning, Advanced Intelligent Systems, 6, 2300899, https://doi.org/10.1002/aisy.202300899, 2024. a
Alian, A., Zareinejad, M., and Talebi, H.: Curvature tracking of a two-segmented soft finger using an adaptive sliding-mode controller, IEEE-ASME T. Mech., 28, 50–59, https://doi.org/10.1109/TMECH.2022.3210030, 2022. a
Ambaye, G., Boldsaikhan, E., and Krishnan, K.: Soft Robot Workspace Estimation via Finite Element Analysis and Machine Learning, in: Actuators, MDPI, vol. 14, p. 110, https://doi.org/10.3390/act14030110, 2025. a
An, X., Cui, Y., Sun, H., Shao, Q., and Zhao, H.: Active-cooling-in-the-loop controller design and implementation for an SMA-driven soft robotic tentacle, IEEE T. Robot., 39, 2325–2341, https://doi.org/10.1109/TRO.2023.3234801, 2023. a
Asawalertsak, N., Heims, F., Kovalev, A., Gorb, S. N., Jørgensen, J., and Manoonpong, P.: Frictional anisotropic locomotion and adaptive neural control for a soft crawling robot, Soft Robot., 10, 545–555, https://doi.org/10.1089/soro.2022.0004, 2023. a
Barbosa, A. S., Tahara, L. Z., and da Silva, M. M.: Motion planning of a fish-like piezoelectric actuated robot using model-based predictive control, J. Vib. Control, 29, 411–427, https://doi.org/10.1177/10775463211048255, 2023. a, b
Bordiga, G., Medina, E., Jafarzadeh, S., Bösch, C., Adams, R. P., Tournat, V., and Bertoldi, K.: Automated discovery of reprogrammable nonlinear dynamic metamaterials, Nat. Mater., 23, 1486–1494, https://doi.org/10.1038/s41563-024-02008-6, 2024. a
Bruder, D., Fu, X., Gillespie, R. B., Remy, C. D., and Vasudevan, R.: Data-driven control of soft robots using Koopman operator theory, IEEE T. Robot., 37, 948–961, https://doi.org/10.1109/TRO.2020.3038693, 2020. a
Cai, C. J., Xiao, X., Kalairaj, M. S., Lee, I. J. J., Mugilvannan, A. K., Yeow, B. S., Tan, J. H., Huang, H., and Ren, H.: Diversified and untethered motion generation via crease patterning from magnetically actuated caterpillar-inspired origami robot, IEEE-ASME T. Mech., 26, 1678–1688, https://doi.org/10.1109/TMECH.2020.3028746, 2020. a
Chen, G., Yang, X., Xu, Y., Lu, Y., and Hu, H.: Neural network-based motion modeling and control of water-actuated soft robotic fish, Smart Mater. Struct., 32, 015004, https://doi.org/10.1088/1361-665X/aca456, 2022a. a
Chen, J., Wang, S., Zhao, Q., Huang, W., Chen, M., Hu, J., Wang, Y., and Liu, H.: Stereo Visual Servoing Control of a Soft Endoscope for Upper Gastrointestinal Endoscopic Submucosal Dissection, Micromachines, 15, 276, https://doi.org/10.3390/mi15020276, 2024. a
Chen, P., Yuan, T., Yu, Y., and Liu, Y.: Design and modeling of a novel soft parallel robot driven by endoskeleton pneumatic artificial muscles, Frontiers of Mechanical Engineering, 17, 22, https://doi.org/10.1007/s11465-022-0678-2, 2022b. a
Chen, Y., Wang, L., Galloway, K., Godage, I., Simaan, N., and Barth, E.: Modal-based kinematics and contact detection of soft robots, Soft Robot., 8, 298–309, https://doi.org/10.1089/soro.2019.0095, 2021. a
Cheng, H., Fang, B., Liu, Q., Zhang, J., and Hong, J.: Attitude control for a bionic soft-robotic-ray via a differential flatness theory and a super-twisting algorithm, J. Frankl. Inst., 361, 107093, https://doi.org/10.1016/j.jfranklin.2024.107093, 2024. a
Cheng, P., Jia, J., Ye, Y., and Wu, C.: Modeling of a soft-rigid gripper actuated by a linear-extension soft pneumatic actuator, Sensors, 21, 493, https://doi.org/10.3390/s21020493, 2021. a
Coevoet, E., Adagolodjo, Y., Lin, M., Duriez, C., and Ficuciello, F.: Planning of soft-rigid hybrid arms in contact with compliant environment: Application to the transrectal biopsy of the prostate, IEEE Robotics and Automation Letters, 7, 4853–4860, https://doi.org/10.1109/LRA.2022.3152322, 2022. a, b
Davy, J., Lloyd, P., Chandler, J. H., and Valdastri, P.: A framework for simulation of magnetic soft robots using the material point method, IEEE Robotics and Automation Letters, 8, 3470–3477, https://doi.org/10.1109/LRA.2023.3268016, 2023. a, b
Demir, S. O., Culha, U., Karacakol, A. C., Pena-Francesch, A., Trimpe, S., and Sitti, M.: Task space adaptation via the learning of gait controllers of magnetic soft millirobots, Int. J. Robot. Res., 40, 1331–1351, https://doi.org/10.1177/02783649211021869, 2021. a
Dou, W., Zhong, G., Yang, J., and Shen, J.: Design and modeling of a hybrid soft robotic manipulator with compliant mechanism, IEEE Robotics and Automation Letters, 8, 2301–2308, https://doi.org/10.1109/LRA.2023.3248485, 2023. a
Drotman, D., Jadhav, S., Sharp, D., Chan, C., and Tolley, M. T.: Electronics-free pneumatic circuits for controlling soft-legged robots, Science Robotics, 6, eaay2627, https://doi.org/10.1126/scirobotics.aay2627, 2021. a
Emet, H., Gür, B., and Dede, M. İ. C.: The design and kinematic representation of a soft robot in a simulation environment, Robotica, 42, 139–152, https://doi.org/10.1017/S026357472300139X, 2024. a
Escribano-Huesca, A., Gila-Vilchez, C., Amaro-da Cruz, A., Leon-Cecilla, A., Palomo, M. G., Ortiz-Ruiz, S., Ruiz, F. G., Moya-Ramirez, I., Lopez-Lopez, M. T., and Rodriguez-Arco, L.: Dynamically Reconfigurable Micro-Patterned Hydrogels Based on Magnetic Pickering Emulsion Droplets, Macromol. Rapid Comm., 45, 2400242, https://doi.org/10.1002/marc.202400242, 2024. a
Fan, W., Wang, J., Zhang, Z., Chen, G., and Wang, H.: Vacuum-driven parallel continuum robots with self-sensing origami linkages, IEEE-ASME T. Mech., 29, 3370–3380, https://doi.org/10.1109/TMECH.2023.3340152, 2024. a, b
Feliu-Talegon, D., Mathew, A. T., Alkayas, A. Y., Adamu, Y. A., and Renda, F.: Dynamic shape estimation of tendon-driven soft manipulators via actuation readings, IEEE Robotics and Automation Letters, https://doi.org/10.1109/LRA.2024.3511406, 2024. a, b
Fernandez-Bussy, S., Chandra, N. C., Koratala, A., Lee-Mateus, A. Y., Barrios-Ruiz, A., Garza-Salas, A., Koirala, T., Funes-Ferrada, R., Balasubramanian, P., Patel, N. M., Chadha, R., Hazelett, B. N., Robertson, K. S., Reisenauer, J., and Abia-Trujillo, D.: Robotic-assisted bronchoscopy: a narrative review of systems, J. Thorac. Dis., 16, 5422, https://doi.org/10.21037/jtd-24-456, 2024. a
Forghani, M., Huang, W., and Jawed, M. K.: Control of uniflagellar soft robots at low reynolds number using buckling instability, J. Dyn. Syst., 143, 061004, https://doi.org/10.1115/1.4049548, 2021. a
Gan, Y., Li, P., Jiang, H., Wang, G., Jin, Y., Chen, X., and Ji, J.: A reinforcement learning method for motion control with constraints on an HPN Arm, IEEE Robotics and Automation Letters, 7, 12006–12013, https://doi.org/10.1109/LRA.2022.3196789, 2022. a
Ghoul, A., Djeffal, S., Wang, H., Kara, K., and Hadjili, M. L.: Enhancing Surgical Robotics: A Dynamic Model and Optimized Control Strategy for Cable-Driven Continuum Robots, J. Mech. Robot., 17, https://doi.org/10.1115/1.4065698, 2025. a
Gough, E., Conn, A. T., and Rossiter, J.: Planning for a tight squeeze: Navigation of morphing soft robots in congested environments, IEEE Robotics and Automation Letters, 6, 4752–4757, https://doi.org/10.1109/LRA.2021.3067594, 2021. a, b
Guo, Q., Wei, J., Yu, S., Feng, X., Gao, Z., Wang, R., Yu, H., Ge, Z., and Yang, W.: Soft robots with different movement capabilities based on photo-responsive multilayer poly (N-isopropylacrylamide) and multi-walled carbon nanotubes, J. Colloid Interf. Sci., 690, 137351, https://doi.org/10.1016/j.jcis.2025.137351, 2025a. a
Guo, Y., Zhu, J., Wang, Y., Zhang, L., Wang, S., Dou, B., Yang, S., Li, B., Niu, F., and Hao, C.: Sea Turtle-inspired Magnetic Soft Robot Demonstrates Versatile Land-to-Submerged Locomotion, J. Bionic Eng., 1–10, https://doi.org/10.1007/s42235-025-00700-0, 2025b. a
Haggerty, D. A., Banks, M. J., Kamenar, E., Cao, A. B., Curtis, P. C., Mezić, I., and Hawkes, E. W.: Control of soft robots with inertial dynamics, Science Robotics, 8, eadd6864, https://doi.org/10.1126/scirobotics.add6864, 2023. a
Han, L., Peng, K., Chen, W., and Liu, Z.: A Data-driven Koopman Modeling Framework With Application to Soft Robots, Int. J. Control Autom., 23, 249–261, https://doi.org/10.1007/s12555-023-0517-1, 2025. a
Han, Z., Liu, Z., He, W., and Li, G.: Distributed parameter modeling and boundary control of an octopus tentacle-inspired soft robot, IEEE T. Contr. Syst. T., 30, 1244–1256, https://doi.org/10.1109/TCST.2021.3104648, 2021. a, b, c
He, S., Sun, L., Xu, Y., and Li, D.: A modeling and data-driven control framework for rigid-soft hybrid robot with visual servoing, IEEE Robotics and Automation Letters, 8, 7281–7288, https://doi.org/10.1109/LRA.2023.3318118, 2023. a, b
Huang, A., Cao, Y., Guo, J., Fang, Z., Su, Y., Liu, S., Yi, J., Wang, H., Dai, J. S., and Wang, Z.: Foam-embedded soft robotic joint with inverse kinematic modeling by iterative self-improving learning, IEEE Robotics and Automation Letters, 9, 1756–1763, https://doi.org/10.1109/LRA.2024.3349831, 2024a. a
Huang, P., Wang, Y., Wu, J., Su, C.-Y., Noda, R., and Fukushima, E. F.: Modeling and Control of Dielectric Elastomer Actuator Based on T–S Fuzzy Model Framework and LMI Theory, Int. J. Fuzzy Syst., 1–13, https://doi.org/10.1007/s40815-024-01911-z, 2025. a
Huang, X., Zhu, X., and Gu, G.: Kinematic modeling and characterization of soft parallel robots, IEEE T. Robot., 38, 3792–3806, https://doi.org/10.1109/TRO.2022.3174474, 2022. a
Huang, X., Rong, Y., and Gu, G.: High-Precision Dynamic Control of Soft Robots With the Physics-Learning Hybrid Modeling Approach, IEEE-ASME T. Mech., https://doi.org/10.1109/TMECH.2024.3403151, 2024b. a
Huang, Y., Ma, R., and Liu, H.: A hybrid force-magnetic control scheme for flexible medical device steering, Mechatronics, 95, 103072, https://doi.org/10.1016/j.mechatronics.2023.103072, 2023. a, b
Ishigaki, T., Ayusawa, K., and Yamamoto, K.: Unified Framework of Gradient Computation for Hybrid-link System and its Dynamical Simulation by Implicit Method, IEEE Robotics and Automation Letters, https://doi.org/10.1109/LRA.2025.3548497, 2025. a
Ju, H., Cha, B., Rus, D., and Lee, J.: Closed-Loop Soft Robot Control Frameworks with Coordinated Policies Based on Reinforcement Learning and Proprioceptive Self-Sensing, Adv. Funct. Mater., 33, 2304642, https://doi.org/10.1002/adfm.202304642, 2023. a
Kalibala, A., Nada, A. A., Ishii, H., and El-Hussieny, H.: Dynamic modelling and predictive position/force control of a plant-inspired growing robot, Bioinspir. Biomim., 20, 016005, https://doi.org/10.1088/1748-3190/ad8e25, 2024. a, b
Keppler, M., Loeffl, F., Wandinger, D., Raschel, C., and Ott, C.: Analyzing the performance limits of articulated soft robots based on the ESPi framework: Applications to damping and impedance control, IEEE Robotics and Automation Letters, 6, 7121–7128, https://doi.org/10.1109/LRA.2021.3097079, 2021. a
Koehler, M., Bieze, T. M., Kruszewski, A., Okamura, A. M., and Duriez, C.: Modeling and control of a 5-DOF parallel continuum haptic device, IEEE T. Robot., 39, 3636–3654, https://doi.org/10.1109/TRO.2023.3277068, 2023. a, b
Kong, F., Zhu, Y., Yang, C., Jin, H., Zhao, J., and Cai, H.: Integrated locomotion and deformation of a magnetic soft robot: Modeling, control, and experiments, IEEE T. Ind. Electron., 68, 5078–5087, https://doi.org/10.1109/TIE.2020.2992000, 2020. a
Koopman, B. O.: Hamiltonian systems and transformation in Hilbert space, P. Natl. Acad. Sci. USA, 17, 315–318, https://doi.org/10.1073/pnas.17.5.315, 1931. a
Kugelstadt, T. and Schömer, E.: Position and orientation based Cosserat rods, in: Symposium on Computer Animation, vol. 11, 169–178, https://dl.acm.org/doi/10.5555/2982818.2982842 (last access: 9 March 2026), 2016. a
Lai, J., Huang, K., Lu, B., Zhao, Q., and Chu, H. K.: Verticalized-tip trajectory tracking of a 3D-printable soft continuum robot: Enabling surgical blood suction automation, IEEE-ASME T. Mech., 27, 1545–1556, https://doi.org/10.1109/TMECH.2021.3090838, 2021. a
Lai, J., Lu, B., Zhao, Q., and Chu, H. K.: Constrained motion planning of a cable-driven soft robot with compressible curvature modeling, IEEE Robotics and Automation Letters, 7, 4813–4820, https://doi.org/10.1109/LRA.2022.3152318, 2022. a, b, c
Li, N., Li, F., Yang, H., and Peng, H.: Real-time control of a soft manipulator based on reduced order extended position-based dynamics, Mech. Mach. Theory, 202, 105774, https://doi.org/10.1016/j.mechmachtheory.2024.105774, 2024. a
Li, P., Zhang, Y., Zhang, G., Zhou, D., and Li, L.: A bioinspired soft robot combining the growth adaptability of vine plants with a coordinated control system, Research, 9843859, https://doi.org/10.34133/2021/9843859, 2021. a
Li, Q., Chen, T., Chen, Y., and Wang, Z.: An underwater bionic crab soft robot with multidirectional controllable motion ability, Ocean Eng., 278, 114412, https://doi.org/10.1016/j.oceaneng.2023.114412, 2023a. a
Li, S., Kruszewski, A., Guerra, T.-M., and Nguyen, A.-T.: Equivalent-input-disturbance-based dynamic tracking control for soft robots via reduced-order finite-element models, IEEE-ASME T. Mech., 27, 4078–4089, https://doi.org/10.1109/TMECH.2022.3144353, 2022. a, b
Li, S., Nguyen, A.-T., Guerra, T.-M., and Kruszewski, A.: Reduced-order model based dynamic tracking for soft manipulators: Data-driven lpv modeling, control design and experimental results, Control Eng. Pract., 138, 105618, https://doi.org/10.1016/j.conengprac.2023.105618, 2023b. a
Li, S., Abdelaziz, S., Wang, L., and Hao, G.: Design and kinetostatic analysis of a tip-stiffness improved compliant continuum robot using anti-buckling universal joints, Int. J. Robot. Res., 02783649251344328, https://doi.org/10.1177/02783649251344328, 2025. a
Li, X. and Wang, D.: Feedback Control of a Dielectric-Elastomer Soft Robot Using Modified Dubins Path Planning, IEEE-ASME T. Mech., https://doi.org/10.1109/TMECH.2024.3427112, 2024. a, b
Li, Z. and Xu, Q.: Multi-Section Magnetic Soft Robot with Multirobot Navigation System for Vasculature Intervention, Cyborg and Bionic Systems, 5, 0188, https://doi.org/10.34133/cbsystems.0188, 2024. a
Liu, J., Jiang, D., Li, Z., He, J., Jian, L., Wang, H., Wen, G., Wang, Z.-P., and Xie, Y. M.: On the nonlinear dynamic performance of a novel bistability-driven soft robot, Adv. Eng. Softw., 206, 103940, https://doi.org/10.1016/j.advengsoft.2025.103940, 2025a. a
Liu, J., Ma, G., Zhang, T., Shan, X., Kang, R., Zheng, R., and Liu, H.: Untethered Soft Crawling Robot Based on Origami Inspired Soft-rigid Hybrid Actuator, J. Bionic Eng., 22, 1071–1084, https://doi.org/10.1007/s42235-025-00682-z, 2025b. a
Liu, W., Duo, Y., Liu, J., Yuan, F., Li, L., Li, L., Wang, G., Chen, B., Wang, S., Yang, H., Liu, Y., Mo, Y., Wang, Y., Fang, B., Sun, F., Ding, X., Zhang, C., and Wen, L.: Touchless interactive teaching of soft robots through flexible bimodal sensory interfaces, Nat. Commun., 13, 5030, https://doi.org/10.1038/s41467-022-32702-5, 2022. a
Liu, Y., Zhang, Y., Dai, X., Wu, R., Zhang, Z., Li, Y., and Zhao, J.: SUTBot: A Soft Umbrella-like Tensegrity Robot with Elastic Struts for In-pipe Locomotion, IEEE Robotics and Automation Letters, https://doi.org/10.1109/LRA.2025.3537866, 2025c. a, b
Long, Y., Zhang, Z., Xu, Z., Gu, E., Lu, Q., Wang, H., and Chen, G.: Lightweight and powerful vacuum-driven gripper with bioinspired elastic spine, IEEE Robotics and Automation Letters, 8, 8136–8143, https://doi.org/10.1109/LRA.2023.3325714, 2023. a
Lotti, N., Xiloyannis, M., Missiroli, F., Bokranz, C., Chiaradia, D., Frisoli, A., Riener, R., and Masia, L.: Myoelectric or force control? A comparative study on a soft arm exosuit, IEEE T. Robot., 38, 1363–1379, https://doi.org/10.1109/TRO.2021.3137748, 2022. a
Lu, X., Xu, W., and Li, X.: Modeling of a soft robotic tongue driven by compressed air, IEEE Access, 11, 61901–61913, https://doi.org/10.1109/ACCESS.2023.3287648, 2023. a, b
Ma, F., Bai, R., Chen, G., and Awtar, S.: Nonlinear Complementary Strain Energy Formulation for Planar Beam Flexures Undergoing Intermediate Deflection, J. Mech. Design, 147, 084501, https://doi.org/10.1115/1.4067869, 2025. a
Ma, J., Han, Z., Liu, Z., Li, G., He, W., and Ge, S. S.: Modeling and control of an octopus inspired soft arm under prescribed spatial motion constraints, J. Intell. Robot. Syst., 109, 94, https://doi.org/10.1007/s10846-023-02026-7, 2023. a
Mamedov, S., Geist, A. R., Viljoen, R., Trimpe, S., and Swevers, J.: Learning deformable linear object dynamics from a single trajectory, IEEE Robotics and Automation Letters, https://doi.org/10.1109/LRA.2025.3577421, 2025. a
Mao, B., Zhou, K., Xiang, Y., Zhang, Y., Yuan, Q., Hao, H., Chen, Y., Liu, H., Wang, X., Wang, X., and Qu, J.: A bioinspired robotic finger for multimodal tactile sensing powered by fiber optic sensors, Advanced Intelligent Systems, 6, 2400175, https://doi.org/10.1002/aisy.202400175, 2024. a
Matia, Y., Kaiser, G. H., Shepherd, R. F., Gat, A. D., Lazarus, N., and Petersen, K. H.: Harnessing nonuniform pressure distributions in soft robotic actuators, Advanced Intelligent Systems, 5, 2200330, https://doi.org/10.1002/aisy.202200330, 2023. a
Mazare, M., Tolu, S., and Taghizadeh, M.: Adaptive variable impedance control for a modular soft robot manipulator in configuration space, Meccanica, 57, 1–15, https://doi.org/10.1007/s11012-021-01436-x, 2022. a
Mendoza, N. and Haghshenas-Jaryani, M.: Combined soft grasping and crawling locomotor robot for exterior navigation of tubular structures, Machines, 12, 157, https://doi.org/10.3390/machines12030157, 2024. a
Meng, H., Zhou, C., Yang, X., Zhao, P., and Zhang, W.: Design and control of a novel pneumatic soft robot based on the improved particle swarm optimization algorithm, PloS One, 20, e0333187, https://doi.org/10.1371/journal.pone.0333187, 2025. a
Moezi, S. A., Sedaghati, R., and Rakheja, S.: Nonlinear dynamic modeling and model-based AI-driven control of a magnetoactive soft continuum robot in a fluidic environment, ISA T., 144, 245–259, https://doi.org/10.1016/j.isatra.2023.10.030, 2024. a
Monje, C. A., Deutschmann, B., Muñoz, J., Ott, C., and Balaguer, C.: Fractional order control of continuum soft robots: Combining decoupled/reduced-dynamics models and robust fractional order controllers for complex soft robot motions, IEEE Contr. Syst. Mag., 43, 66–99, https://doi.org/10.1109/MCS.2023.3253420, 2023. a, b
Morales-Enríquez, D. A., Guzmán-López, J., Aguilar-Ramírez, R. A., Lorenzo-Martínez, J. L., Sapién-Garza, D., Cortez, R., Lozada-Castillo, N., and Luviano-Juárez, A.: Data-Based Modeling and Control of a Single Link Soft Robotic Arm, Biomimetics, 10, 294, https://doi.org/10.3390/biomimetics10050294, 2025. a, b, c
Morasso, P.: A computational model of hybrid trunk-like robots for synergy formation in anticipation of physical interaction, Biomimetics, 10, 21, https://doi.org/10.3390/biomimetics10010021, 2025. a
Ochoa, O., Méndez, E., Perez-Santiago, R., Cuan-Urquizo, E., Sandoval-Castro, X. Y., and González, A.: Reconfigurable Soft Pneumatic Actuators with Metamaterial Reinforcements: Tunable Stiffness and Deformation Modes, Mater. Design, 113649, https://doi.org/10.1016/j.matdes.2025.113649, 2025. a, b
Papadakis, E., Tsakiris, D. P., and Sfakiotakis, M.: An Octopus-Inspired Soft Pneumatic Robotic Arm, Biomimetics, 9, 773, https://doi.org/10.3390/biomimetics9120773, 2024. a, b
Papageorgiou, D., Sigurðardóttir, G. Þ., Falotico, E., and Tolu, S.: Sliding-mode control of a soft robot based on data-driven sparse identification, Control Eng. Pract, 144, 105836, https://doi.org/10.1016/j.conengprac.2023.105836, 2024. a
Patterson, Z. J., Sabelhaus, A. P., and Majidi, C.: Robust control of a multi-axis shape memory alloy-driven soft manipulator, IEEE Robotics and Automation Letters, 7, 2210–2217, https://doi.org/10.1109/LRA.2022.3143256, 2022. a
Patterson, Z. J., Astley, H. C., and Majidi, C.: Soft robotic brittle star shows the influence of mass distribution on underwater walking, Bioinspir. Biomim., 20, 036003, https://doi.org/10.1088/1748-3190/adbecb, 2025. a
Pierallini, M., Gopanunni, R. K. M., Angelini, F., Bicchi, A., and Garabini, M.: Fishing for data: Modeling, optimal planning, and iterative learning control for flexible link robots, IEEE T. Contr. Syst. T., https://doi.org/10.1109/TCST.2025.3550030, 2025. a
Pilch, S., Klug, D., and Sawodny, O.: Optimal Interaction-friendly Trajectory Generation of a Pneumatic Soft Continuum Manipulator, in: 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 1189–1196, IEEE, https://doi.org/10.1109/AIM55361.2024.10637105, 2024. a
Piqué, F., Kalidindi, H. T., Fruzzetti, L., Laschi, C., Menciassi, A., and Falotico, E.: Controlling soft robotic arms using continual learning, IEEE Robotics and Automation Letters, 7, 5469–5476, https://doi.org/10.1109/LRA.2022.3157369, 2022. a
Prigozin, A. and Degani, A.: Interacting with Obstacles Using a Bio-Inspired, Flexible, Underactuated Multilink Manipulator, Biomimetics, 9, 86, https://doi.org/10.3390/biomimetics9020086, 2024. a
Pustina, P., Borja, P., Della Santina, C., and De Luca, A.: P-satI-D shape regulation of soft robots, IEEE Robotics and Automation Letters, 8, 1–8, https://doi.org/10.1109/LRA.2022.3221304, 2022a. a, b
Pustina, P., Della Santina, C., and De Luca, A.: Feedback regulation of elastically decoupled underactuated soft robots, IEEE Robotics and Automation Letters, 7, 4512–4519, https://doi.org/10.1109/LRA.2022.3150829, 2022b. a
Ramos-Velasco, L., Parra-Vega, V., García-Rodríguez, R., Vega-Navarrete, M., Trejo-Ramos, C., and Olguín-Díaz, E.: Knowledge-based self-tuning of PID control gains for continuum soft robots, Eng. Appl. Artif. Intel., 133, 108447, https://doi.org/10.1016/j.engappai.2024.108447, 2024. a
Rao, P., Peyron, Q., and Burgner-Kahrs, J.: Shape representation and modeling of tendon-driven continuum robots using euler arc splines, IEEE Robotics and Automation Letters, 7, 8114–8121, https://doi.org/10.1109/LRA.2022.3185377, 2022. a, b
Rao, P., Pogue, C., Peyron, Q., Diller, E., and Burgner-Kahrs, J.: Modeling and analysis of tendon-driven continuum robots for rod-based locking, IEEE Robotics and Automation Letters, 8, 3126–3133, https://doi.org/10.1109/LRA.2023.3264869, 2023. a
Robbins, A. S., Ho, M., and Teodorescu, M.: Model-free dynamic control of robotic joints with integrated elastic ligaments, Robotics and Autonomous Systems, 155, 104150, https://doi.org/10.1016/j.robot.2022.104150, 2022. a
Rong, Y. and Gu, G.: Vision-based real-time shape estimation of self-occluding soft parallel robots using neural networks, IEEE Robotics and Automation Letters, 9, 7349–7356, https://doi.org/10.1109/LRA.2024.3421794, 2024. a
Roshanfar, M., Dargahi, J., and Hooshiar, A.: Cosserat rod-based dynamic modeling of a hybrid-actuated soft robot for robot-assisted cardiac ablation, in: Actuators, MDPI, vol. 13, p. 8, https://doi.org/10.3390/act13010008, 2023. a, b, c
Satake, Y. and Ishii, H.: Path planning method with constant bending angle constraint for soft growing robot using heat welding mechanism, IEEE Robotics and Automation Letters, 8, 2836–2843, https://doi.org/10.1109/LRA.2023.3260705, 2023. a, b
Sayahkarajy, M., Witte, H., and Faudzi, A. A. M.: Chorda dorsalis system as a paragon for soft medical robots to design echocardiography probes with a new SOM-based steering control, Biomimetics, 9, 199, https://doi.org/10.3390/biomimetics9040199, 2024. a
Scharff, R. B., Fang, G., Tian, Y., Wu, J., Geraedts, J. M., and Wang, C. C.: Sensing and reconstruction of 3-D deformation on pneumatic soft robots, IEEE-ASME T. Mech., 26, 1877–1885, https://doi.org/10.1109/TMECH.2021.3078263, 2021. a
Seleem, I. A., El-Hussieny, H., and Ishii, H.: Imitation-based motion planning and control of a multi-section continuum robot interacting with the environment, IEEE Robotics and Automation Letters, 8, 1351–1358, https://doi.org/10.1109/LRA.2023.3239306, 2023. a, b, c, d
Shabana, A. A. and Eldeeb, A. E.: Motion and shape control of soft robots and materials, Nonlinear Dynam., 104, 165–189, https://doi.org/10.1007/s11071-021-06272-y, 2021. a
Shao, X., Pustina, P., Stölzle, M., Sun, G., De Luca, A., Wu, L., and Della Santina, C.: Model-based control for soft robots with system uncertainties and input saturation, IEEE T. Ind. Electron., 71, 7435–7444, https://doi.org/10.1109/TIE.2023.3303636, 2023. a
Shen, Y. and Zou, Y.: Contact Dynamic Behaviors of Magnetic Hydrogel Soft Robots, Gels, 11, 20, https://doi.org/10.3390/gels11010020, 2024. a, b
Shi, R., Fang, H., and Xu, J.: Continuum modeling and dynamics of earthworm-like peristaltic locomotion, J. Mech. Phys. Solids, 196, 106034, https://doi.org/10.1016/j.jmps.2025.106034, 2025. a
Soon, R. H., Yin, Z., Dogan, M. A., Dogan, N. O., Tiryaki, M. E., Karacakol, A. C., Aydin, A., Esmaeili-Dokht, P., and Sitti, M.: Pangolin-inspired untethered magnetic robot for on-demand biomedical heating applications, Nat. Commun., 14, 3320, https://doi.org/10.1038/s41467-023-38689-x, 2023. a
Sun, X., Nose, A., and Kohsaka, H.: A vacuum-actuated soft robot inspired by Drosophila larvae to study kinetics of crawling behaviour, Plos One, 18, e0283316, https://doi.org/10.1371/journal.pone.0283316, 2023. a
Taguchi, R. and Sawada, Y.: Autonomous grasping system with a tendon parallel manipulator consisting of rib-reinforced vacuum-driven linear actuators and a soft robot hand with a time-of-flight camera, Adv. Robotics, 1–16, https://doi.org/10.1080/01691864.2025.2508783, 2025. a, b
Tanaka, K., Minami, Y., Tokudome, Y., Inoue, K., Kuniyoshi, Y., and Nakajima, K.: Continuum-body-pose estimation from partial sensor information using recurrent neural networks, IEEE Robotics and Automation Letters, 7, 11244–11251, https://doi.org/10.1109/LRA.2022.3199034, 2022. a
Tang, Z., Xin, W., Wang, P., and Laschi, C.: Learning-based control for soft robot–environment interaction with force/position tracking capability, Soft Robot., 11, 767–778, https://doi.org/10.1089/soro.2023.0116, 2024. a
Tao, J., Li, T., Hu, Q., and Dong, E.: Soft Origami Continuum Robot Capable of Precise Motion through Machine Learning, IEEE Robotics and Automation Letters, https://doi.org/10.1109/LRA.2024.3518106, 2024. a
Tian, C., Fan, X., Jia, J., Yang, Z., and Xie, H.: An automatic navigation framework for magnetic fish-like millirobot in uncertain dynamic environments, IEEE Robotics and Automation Letters, https://doi.org/10.1109/LRA.2025.3531150, 2025. a, b
Troeung, C., Liu, S., and Chen, C.: Modelling of tendon-driven continuum robot based on constraint analysis and pseudo-rigid body model, IEEE Robotics and Automation Letters, 8, 989–996, https://doi.org/10.1109/LRA.2023.3234821, 2023. a, b
Troise, M., Gaidano, M., Palmieri, P., and Mauro, S.: Preliminary analysis of a lightweight and deployable soft robot for space applications, Appl. Sci., 11, 2558, https://doi.org/10.3390/app11062558, 2021. a, b
Wan, Z., Sun, Y., Qin, Y., Skorina, E. H., Gasoto, R., Luo, M., Fu, J., and Onal, C. D.: Design, analysis, and real-time simulation of a 3D soft robotic snake, Soft Robot., 10, 258–268, https://doi.org/10.1089/soro.2021.0144, 2023. a
Wang, C., Wang, T., Li, M., Zhang, R., Ugurlu, H., and Sitti, M.: Heterogeneous multiple soft millirobots in three-dimensional lumens, Science Advances, 10, eadq1951, https://doi.org/10.1126/sciadv.adq1951, 2024a. a
Wang, J., Li, Y., Tian, S., Zhao, Y., Ren, G., Xi, F., and Zhao, Y.: Bio-inspired rigid-flexible coupled skin-scales system for enhanced protection in soft robotic fish, npj Robotics, 3, 35, https://doi.org/10.1038/s44182-025-00052-1, 2025a. a
Wang, K., Wang, X., Ho, J. D.-L., Fang, G., Zhu, B., Xie, R., Liu, Y.-H., Au, K. W. S., Chan, J. Y.-K., and Kwok, K.-W.: A fast soft robotic laser sweeping system using data-driven modeling approach, IEEE T. Robot., 39, 3043–3058, https://doi.org/10.1109/TRO.2023.3262118, 2023. a
Wang, T., Yao, S., and Zhu, S.: Energy-saving trajectory optimization of a fluidic soft robotic arm, Smart Mater. Struct., 31, 115011, https://doi.org/10.1088/1361-665X/ac9768, 2022. a
Wang, T., Wang, X., Fu, G., and Lu, C.: A dynamic modeling method for soft pneumatic robotic arms considering both geometric nonlinearity and visco-hyperelasticity, Int. J. Struct. Stab. Dy., 24, 2450271, https://doi.org/10.1142/S0219455424502717, 2024b. a
Wang, X., Lin, A., Yuan, W., Hu, H., Cheng, G., and Ding, J.: Design of an actuator with bionic claw hook–suction cup hybrid structure for soft robot, Bioinspir. Biomim., 19, 036021, https://doi.org/10.1088/1748-3190/ad3ff7, 2024c. a
Wang, Z., Bhattacharjee, A., Duygu, Y. C., Lee, S., Jabbarzadeh, M., Fu, H. C., and Kim, M. J.: Design, modeling, and control of magnetically actuated rod-like soft robots: Propulsion in free space with viscous fluids and navigation in confined geometries, Sensor. Actuat. A-Phys., 387, 116305, https://doi.org/10.1016/j.sna.2025.116305, 2025b. a
Wei, F., Luo, K., Zhang, Y., and Jiang, J.: Structural Design and Kinematic Analysis of Cable-Driven soft robot, in: Actuators, MDPI, vol. 13, p. 497, https://doi.org/10.3390/act13120497, 2024. a, b, c
Wiese, M., Berthold, R., Wangenheim, M., and Raatz, A.: Describing and analyzing mechanical contact for continuum robots using a shooting-based cosserat rod implementation, IEEE Robotics and Automation Letters, 9, 1668–1675, https://doi.org/10.1109/LRA.2023.3346272, 2023. a
Wockenfuß, W. R., Brandt, V., Weisheit, L., and Drossel, W.-G.: Design, modeling and validation of a tendon-driven soft continuum robot for planar motion based on variable stiffness structures, IEEE Robotics and Automation Letters, 7, 3985–3991, https://doi.org/10.1109/LRA.2022.3149031, 2022. a
Wu, C., Zhang, R., Tang, F., Zhao, P., Li, L., and Zhang, D.: Simulation-data-driven vibration optimization of deployable stepwise composite booms, Alexandria Engineering Journal, 114, 440–452, https://doi.org/10.1016/j.aej.2024.11.085, 2025a. a
Wu, C., Zhao, P., Chen, P., Ni, Z., Yue, S., Li, L., Zhang, D., and Hao, G.: Design, dynamic modelling and experimental study on a tether-net system for active debris removal, Mech. Mach. Theory, 208, 105958, https://doi.org/10.1016/j.mechmachtheory.2025.105958, 2025b. a
Wu, K., Zheng, G., and Zhang, J.: FEM-based trajectory tracking control of a soft trunk robot, Robot. Auton. Syst., 150, 103961, https://doi.org/10.1016/j.robot.2021.103961, 2022. a
Xiao, Q., Musa, M., Godage, I. S., Su, H., and Chen, Y.: Kinematics and stiffness modeling of soft robot with a concentric backbone, J. Mech. Robot., 15, 051011, https://doi.org/10.1115/1.4055860, 2023. a, b
Xie, Q., Wang, T., and Zhu, S.: Simplified dynamical model and experimental verification of an underwater hydraulic soft robotic arm, Smart Mater. Struct., 31, 075 011, https://doi.org/10.1088/1361-665X/ac736f, 2022. a
Xie, Q., Zhang, Y., Wang, T., and Zhu, S.: Dynamic response prediction of hydraulic soft robotic arms based on LSTM neural network, P. I. Mech. Eng. I-J. Sys., 237, 1251–1265, https://doi.org/10.1177/09596518231153446, 2023. a
Xing, Z. and Yong, H.: Dynamic analysis and active control of hard-magnetic soft materials, International Journal of Smart and Nano Materials, 12, 429–449, https://doi.org/10.1080/19475411.2021.1961909, 2021. a, b
Xu, F., Ma, K., Jiang, Q., and Jiang, G.-P.: Kinematic modelling and experimental testing of a particle-jamming soft robot based on a DEM-FEM coupling method, Bioinspir. Biomim., 18, 046018, https://doi.org/10.1088/1748-3190/acdc73, 2023. a
Xu, F., Kang, X., and Wang, H.: Hybrid visual servoing control of a soft robot with compliant obstacle avoidance, IEEE-ASME T. Mech., 29, 4446–4455, https://doi.org/10.1109/TMECH.2024.3377632, 2024. a, b
Xu, N. W., Townsend, J. P., Costello, J. H., Colin, S. P., Gemmell, B. J., and Dabiri, J. O.: Field testing of biohybrid robotic jellyfish to demonstrate enhanced swimming speeds, Biomimetics, 5, 64, https://doi.org/10.3390/biomimetics5040064, 2020. a
Xu, Y., Li, H., Li, H., Fang, G., and Jia, H.: Path planning and intelligent control of a soft robot arm based on gas-structure coupling actuators, Frontiers in Materials, 9, 1052538, https://doi.org/10.3389/fmats.2022.1052538, 2022. a, b, c
Yang, P., Huang, B., McCoul, D., Xie, D., Li, M., and Zhao, J.: SpringWorm: A soft crawling robot with a large-range omnidirectional deformable rectangular spring for control rod drive mechanism inspection, Soft Robot., 10, 280–291, https://doi.org/10.1089/soro.2021.0127, 2023a. a
Yang, Q. and Liu, Z.: Model-based versus model-free optimal tracking for soft robots: analytical and data-driven Koopman modeling, control design and experimental validation, Nonlinear Dynam., 112, 15267–15287, https://doi.org/10.1007/s11071-024-09851-x, 2024. a, b
Yang, Y., Han, J., Liu, Z., Zhao, Z., and Hong, K.-S.: Modeling and adaptive neural network control for a soft robotic arm with prescribed motion constraints, IEEE/CAA Journal of Automatica Sinica, 10, 501–511, https://doi.org/10.1109/JAS.2023.123213, 2023b. a, b, c
Zhang, A., Truby, R. L., Chin, L., Li, S., and Rus, D.: Vision-based sensing for electrically-driven soft actuators, IEEE Robotics and Automation Letters, 7, 11509–11516, https://doi.org/10.1109/LRA.2022.3201604, 2022. a
Zhang, H., Heung, K. H., Naquila, G. J., Hingwe, A., and Deshpande, A. D.: A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo-Rigid Body Modeling, Advanced Intelligent Systems, 7, 2400637, https://doi.org/10.1002/aisy.202400637, 2025. a
Zhang, J., Chen, X., Stegagno, P., Zhou, M., and Yuan, C.: Learning-based tracking control of soft robots, IEEE Robotics and Automation Letters, 8, 6155–6162, https://doi.org/10.1109/LRA.2023.3303724, 2023. a
Zhang, J., Chen, Y., and Gong, Y.: Kinematics, dynamics, and control of underwater robotic snake based on a rigid-soft unified model, Ships Offshore Struc., 19, 1408–1423, https://doi.org/10.1080/17445302.2023.2247131, 2024a. a
Zhang, L., Xu, Q., and Liu, J.: Dynamic modeling for continuous locomotion of bionic soft worm-like robot, Int. J. Non-Lin. Mech., 162, 104702, https://doi.org/10.1016/j.ijnonlinmec.2024.104702, 2024b. a
Zhang, S., Shan, J., Fang, B., and Sun, F.: Soft robotic finger embedded with visual sensor for bending perception, Robotica, 39, 378–390, https://doi.org/10.1017/S0263574720000429, 2021a. a
Zhang, Y., Yang, D., Yan, P., Zhou, P., Zou, J., and Gu, G.: Inchworm inspired multimodal soft robots with crawling, climbing, and transitioning locomotion, IEEE T. Robot., 38, 1806–1819, https://doi.org/10.1109/TRO.2021.3115257, 2021b. a
Zhao, P., Liu, J., Wu, C., Zhao, Y., Yu, S., and Jing, X.: Theoretical modelling and sensitivity analysis for triangular membrane wrinkling of solar sail under muti-field effects, Appl. Math. Model., 116646, https://doi.org/10.1016/j.apm.2025.116646, 2025. a
Zhao, Y., Xi, F., Hao, G., Tian, Y., Li, L., and Wang, J.: Kineto-static analysis of a hybrid manipulator consisting of rigid and flexible limbs with locking function for planar shape morphing, Mech. Mach. Theory, 203, 105802, https://doi.org/10.1016/j.mechmachtheory.2024.105802, 2024. a
Zhou, X., Wu, C., Cai, S., and Bao, G.: A Pneumatic-Actuated Flexible Robotic Arm With Rigid Backbone for High-Precision and Safe Manipulation, IEEE-ASME T. Mech., https://doi.org/10.1109/TMECH.2024.3392828, 2024. a
Short summary
Soft robots promise healthcare and exploration gains, but nonlinear ever-bending bodies defy control. This paper reviews recent advances in diverse structures and actuation mechanisms, modeling approaches integrating classical and modern techniques, trajectory planning balancing obstacle avoidance and optimization, and control strategies covering both model-based and model-free methods. Future work must overcome bottlenecks such as low actuation efficiency and poor real-time performance.
Soft robots promise healthcare and exploration gains, but nonlinear ever-bending bodies defy...
