[1] [1] M. Lu, J. Yin, Q. Zhu, G. Lin, M. Mou et al., Artificial intelligence in pharmaceutical sciences. Engineering 27, 37–69 (2023). 10.1016/j.eng.2023.01.014

[2] [2] J. Sipola, M. Saunila, J. Ukko, Adopting artificial intelligence in sustainable business. J. Clean. Prod. 426, 139197 (2023). 10.1016/j.jclepro.2023.139197

[3] [3] J. Sourati, J.A. Evans, Accelerating science with human-aware artificial intelligence. Nat. Hum. Behav. 7, 1682–1696 (2023). 10.1038/s41562-023-01648-z

[4] [4] X. Bi, L. Lin, Z. Chen, J. Ye, Artificial intelligence for surface-enhanced Raman spectroscopy. Small Methods 8, e2301243 (2024). 10.1002/smtd.202301243

[5] [5] S. Stamenković, N. Jovanović, B. Vasović, M. Cvjetković, Z. Jovanović, Software tools for learning artificial intelligence algorithms. Artif. Intell. Rev. 56, 10297–10326 (2023). 10.1007/s10462-023-10436-0

[6] [6] S. Ali, T. Abuhmed, S. El-Sappagh, K. Muhammad, J.M. Alonso-Moral et al., Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy Artificial Intelligence. Inf. Fusion 99, 101805 (2023). 10.1016/j.inffus.2023.101805

[7] [7] B.J. Hayes, C. Chen, O. Powell, E. Dinglasan, K. Villiers et al., Advancing artificial intelligence to help feed the world. Nat. Biotechnol. 41, 1188–1189 (2023). 10.1038/s41587-023-01898-2

[8] [8] J. Kim, J.H. Kim, C. Kim, J. Park, Decisions with ChatGPT: reexamining choice overload in ChatGPT recommendations. J. Retail. Consum. Serv. 75, 103494 (2023). 10.1016/j.jretconser.2023.103494

[9] [9] M. Francl, ChatGPT saves the day. Nat. Chem. 15(7), 890–891 (2023). 10.1038/s41557-023-01253-7

[10] [10] J.-J. Zhu, J. Jiang, M. Yang, Z.J. Ren, ChatGPT and environmental research. Environ. Sci. Technol. 57, 17667–17670 (2023). 10.1021/acs.est.3c01818

[11] [11] F. Gilardi, M. Alizadeh, M. Kubli, ChatGPT outperforms crowd workers for text-annotation tasks. Proc. Natl. Acad. Sci. U.S.A. 120, e2305016120 (2023). 10.1073/pnas.2305016120

[12] [12] Z. Sun, M. Zhu, Z. Zhang, Z. Chen, Q. Shi et al., Artificial intelligence of things (AIoT) enabled virtual shop applications using self-powered sensor enhanced soft robotic manipulator. Adv. Sci. 8, e2100230 (2021). 10.1002/advs.202100230

[13] [13] M. Li, A. Pal, A. Aghakhani, A. Pena-Francesch, M. Sitti, Soft actuators for real-world applications. Nat. Rev. Mater. 7, 235–249 (2022). 10.1038/s41578-021-00389-7

[14] [14] V. Sanchez, C.J. Walsh, R.J. Wood, Textile technology for soft robotic and autonomous garments. Adv. Funct. Mater. 31, 2008278 (2021). 10.1002/adfm.202008278

[15] [15] G. Gu, N. Zhang, C. Chen, H. Xu, X. Zhu, Soft robotics enables neuroprosthetic hand design. ACS Nano 17, 9661–9672 (2023). 10.1021/acsnano.3c01474

[16] [16] D.S. Shah, J.P. Powers, L.G. Tilton, S. Kriegman, J. Bongard et al., A soft robot that adapts to environments through shape change. Nat. Mach. Intell. 3, 51–59 (2021). 10.1038/s42256-020-00263-1

[17] [17] Z. Ren, R. Zhang, R.H. Soon, Z. Liu, W. Hu et al., Soft-bodied adaptive multimodal locomotion strategies in fluid-filled confined spaces. Sci. Adv. 7, eabh2022 (2021). 10.1126/sciadv.abh2022

[18] [18] Y. Yu, J. Li, S.A. Solomon, J. Min, J. Tu et al., All-printed soft human-machine interface for robotic physicochemical sensing. Sci. Robot. 7, eabn0495 (2022). 10.1126/scirobotics.abn0495

[19] [19] H. Wang, M. Totaro, L. Beccai, Toward perceptive soft robots: progress and challenges. Adv. Sci. 5, 1800541 (2018). 10.1002/advs.201800541

[20] [20] D. Rus, M.T. Tolley, Design, fabrication and control of soft robots. Nature 521, 467–475 (2015). 10.1038/nature14543

[21] [21] H.K. Yap, H.Y. Ng, C.-H. Yeow, High-force soft printable pneumatics for soft robotic applications. Soft Robot. 3, 144–158 (2016). 10.1089/soro.2016.0030

[22] [22] C. Majidi, Soft robotics: a perspective—current trends and prospects for the future. Soft Robot. 1, 5–11 (2014). 10.1089/soro.2013.0001

[23] [23] S. Shu, Z. Wang, P. Chen, J. Zhong, W. Tang et al., Machine-learning assisted electronic skins capable of proprioception and exteroception in soft robotics. Adv. Mater. 35, e2211385 (2023). 10.1002/adma.202211385

[24] [24] Q. Ji, S. Fu, K. Tan, S. ThorapalliMuralidharan, K. Lagrelius et al., Synthesizing the optimal gait of a quadruped robot with soft actuators using deep reinforcement learning. Robot. Comput. Integr. Manuf. 78, 102382 (2022). 10.1016/j.rcim.2022.102382

[25] [25] M. Mohammadi, A.Z. Kouzani, M. Bodaghi, J. Long, S.Y. Khoo et al., Sustainable robotic joints 4D printing with variable stiffness using reinforcement learning. Robot. Comput. Integr. Manuf. 85, 102636 (2024). 10.1016/j.rcim.2023.102636

[26] [26] Y. Wang, H. Wu, L. Xu, H. Zhang, Y. Yang et al., Hierarchically patterned self-powered sensors for multifunctional tactile sensing. Sci. Adv. 6, eabb9083 (2020). 10.1126/sciadv.abb9083

[27] [27] X. Ma, Y. Ye, H. Meng, W. Wang, W. Wang et al., Sensor embedded soft fingertip for precise manipulation and softness recognition. IEEE Robot. Autom. Lett. 6, 8734–8741 (2021). 10.1109/LRA.2021.3115156

[28] [28] K. Pang, X. Song, Z. Xu, X. Liu, Y. Liu et al., Hydroplastic foaming of graphene aerogels and artificially intelligent tactile sensors. Sci. Adv. 6, eabd4045 (2020). 10.1126/sciadv.abd4045

[29] [29] S. Sundaram, P. Kellnhofer, Y. Li, J.-Y. Zhu, A. Torralba et al., Learning the signatures of the human grasp using a scalable tactile glove. Nature 569, 698–702 (2019). 10.1038/s41586-019-1234-z

[30] [30] S. Chun, W. Son, H. Kim, S.K. Lim, C. Pang et al., Self-powered pressure- and vibration-sensitive tactile sensors for learning technique-based neural finger skin. Nano Lett. 19, 3305–3312 (2019). 10.1021/acs.nanolett.9b00922

[31] [31] H. Lu, Y. Hong, Y. Yang, Z. Yang, Y. Shen, Battery-less soft millirobot that can move, sense, and communicate remotely by coupling the magnetic and piezoelectric effects. Adv. Sci. 7, 2000069 (2020). 10.1002/advs.202000069

[32] [32] W. Guo, X. Sheng, H. Liu, X. Zhu, Toward an enhanced human–machine interface for upper-limb prosthesis control with combined EMG and NIRS signals. IEEE Trans. Hum. Mach. Syst. 47, 564–575 (2017). 10.1109/THMS.2016.2641389

[33] [33] K. Sim, Z. Rao, Z. Zou, F. Ershad, J. Lei et al., Metal oxide semiconductor nanomembrane-based soft unnoticeable multifunctional electronics for wearable human-machine interfaces. Sci. Adv. 5, eaav9653 (2019). 10.1126/sciadv.aav9653

[34] [34] T. Jin, Z. Sun, L. Li, Q. Zhang, M. Zhu et al., Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications. Nat. Commun. 11, 5381 (2020). 10.1038/s41467-020-19059-3

[35] [35] Y. Shi, F. Wang, J. Tian, S. Li, E. Fu et al., Self-powered electro-tactile system for virtual tactile experiences. Sci. Adv. 7, eabe2943 (2021). 10.1126/sciadv.abe2943

[36] [36] X. Han, X. Chen, X. Tang, Y.-L. Chen, J.-H. Liu et al., Flexible polymer transducers for dynamic recognizing physiological signals. Adv. Funct. Mater. 26, 3640–3648 (2016). 10.1002/adfm.201600008

[37] [37] F. Wang, P. Jin, Y. Feng, J. Fu, P. Wang et al., Flexible Doppler ultrasound device for the monitoring of blood flow velocity. Sci. Adv. 7, eabi9283 (2021). 10.1126/sciadv.abi9283

[38] [38] C. Qu, S. Wang, L. Liu, Y. Bai, L. Li et al., Bioinspired flexible volatile organic compounds sensor based on dynamic surface wrinkling with dual-signal response. Small 15, e1900216 (2019). 10.1002/smll.201900216

[39] [39] Y. Su, C. Dagdeviren, R. Li, Measured output voltages of piezoelectric devices depend on the resistance of voltmeter. Adv. Funct. Mater. 25, 5320–5325 (2015). 10.1002/adfm.201502280

[40] [40] Y. Qiu, C. Wang, X. Lu, H. Wu, X. Ma et al., A biomimetic Drosera capensis with adaptive decision-predation behavior based on multifunctional sensing and fast actuating capability. Adv. Funct. Mater. 32, 2270077 (2022). 10.1002/adfm.202270077

[41] [41] Y. Jin, G. Chen, K. Lao, S. Li, Y. Lu et al., Identifying human body states by using a flexible integrated sensor. npj Flex. Electron. 4, 28 (2020). 10.1038/s41528-020-00090-9

[42] [42] Q. Wang, S. Ling, X. Liang, H. Wang, H. Lu et al., Self-healable multifunctional electronic tattoos based on silk and graphene. Adv. Funct. Mater. 29, 1808695 (2019). 10.1002/adfm.201808695

[43] [43] Y. Qiu, S. Sun, C. Xu, Y. Wang, Y. Tian et al., The frequency-response behaviour of flexible piezoelectric devices for detecting the magnitude and loading rate of stimuli. J. Mater. Chem. C 9, 584–594 (2021). 10.1039/D0TC02949A

[44] [44] N. Bai, L. Wang, Q. Wang, J. Deng, Y. Wang et al., Graded intrafillable architecture-based iontronic pressure sensor with ultra-broad-range high sensitivity. Nat. Commun. 11, 209 (2020). 10.1038/s41467-019-14054-9

[45] [45] X.Q. Wang, K.H. Chan, Y. Cheng, T. Ding, T. Li et al., Somatosensory, light-driven, thin-film robots capable of integrated perception and motility. Adv. Mater. 32, e2000351 (2020). 10.1002/adma.202000351

[46] [46] L. Cheng, W. Qian, L. Wei, H. Zhang, T. Zhao et al., A highly sensitive piezoresistive sensor with interlocked graphene microarrays for meticulous monitoring of human motions. J. Mater. Chem. C 8, 11525–11531 (2020). 10.1039/D0TC02539A

[47] [47] S. Li, G. Liu, R. Li, Q. Li, Y. Zhao et al., Contact-resistance-free stretchable strain sensors with high repeatability and linearity. ACS Nano 16, 541–553 (2022). 10.1021/acsnano.1c07645

[48] [48] Y. Zhao, C.-Y. Lo, L. Ruan, C.-H. Pi, C. Kim et al., Somatosensory actuator based on stretchable conductive photothermally responsive hydrogel. Sci. Robot. 6, eabd5483 (2021). 10.1126/scirobotics.abd5483

[49] [49] D. Li, S. Yang, X. Chen, W.-Y. Lai, W. Huang, 3D wearable fabric-based micro-supercapacitors with ultra-high areal capacitance. Adv. Funct. Mater. 31, 2107484 (2021). 10.1002/adfm.202107484

[50] [50] G. Li, S. Liu, L. Wang, R. Zhu, Skin-inspired quadruple tactile sensors integrated on a robot hand enable object recognition. Sci. Robot. 5, eabc8134 (2020). 10.1126/scirobotics.abc8134

[51] [51] L. Mo, D. Zhang, X. Fu, J. Dou, K. Li et al., A multidirectional external perception soft actuator based on flexible optical waveguide for underwater teleoperation. Adv. Intell. Syst. 5, 2300029 (2023). 10.1002/aisy.202300029

[52] [52] S. Terryn, J. Brancart, D. Lefeber, G. Van Assche, B. Vanderborght, Self-healing soft pneumatic robots. Sci. Robot. 2, 4268 (2017). 10.1126/scirobotics.aan4268

[53] [53] I.M. Van Meerbeek, C.M. De Sa, R.F. Shepherd, Soft optoelectronic sensory foams with proprioception. Sci. Robot. 3, eaau2489 (2018). 10.1126/scirobotics.aau2489

[54] [54] B. Ying, R.Z. Chen, R. Zuo, J. Li, X. Liu et al., An anti-freezing, ambient-stable and highly stretchable ionic skin with strong surface adhesion for wearable sensing and soft robotics. Adv. Funct. Mater. 31, 2104665 (2021). 10.1002/adfm.202104665

[55] [55] R.F. Shepherd, F. Ilievski, W. Choi, S.A. Morin, A.A. Stokes et al., Multigait soft robot. Proc. Natl. Acad. Sci. U.S.A. 108, 20400–20403 (2011). 10.1073/pnas.1116564108

[56] [56] Y. Wu, J.K. Yim, J. Liang, Z. Shao, M. Qi et al., Insect-scale fast moving and ultrarobust soft robot. Sci. Robot. 4, eaax1594 (2019). 10.1126/scirobotics.aax1594

[57] [57] X. Yang, L. Chang, N.O. Pérez-Arancibia, An 88-milligram insect-scale autonomous crawling robot driven by a catalytic artificial muscle. Sci. Robot. 5, eaba0015 (2020). 10.1126/scirobotics.aba0015

[58] [58] J. Kim, M. Lee, H.J. Shim, R. Ghaffari, H.R. Cho et al., Stretchable silicon nanoribbon electronics for skin prosthesis. Nat. Commun. 5, 5747 (2014). 10.1038/ncomms6747

[59] [59] C.G. Núñez, W.T. Navaraj, E.O. Polat, R. Dahiya, Energy-autonomous, flexible, and transparent tactile skin. Adv. Funct. Mater. 27, 1606287 (2017). 10.1002/adfm.201606287

[60] [60] Y. Pang, X. Xu, S. Chen, Y. Fang, X. Shi et al., Skin-inspired textile-based tactile sensors enable multifunctional sensing of wearables and soft robots. Nano Energy 96, 107137 (2022). 10.1016/j.nanoen.2022.107137

[61] [61] Y. Cheng, D. Wu, S. Hao, Y. Jie, X. Cao et al., Highly stretchable triboelectric tactile sensor for electronic skin. Nano Energy 64, 103907 (2019). 10.1016/j.nanoen.2019.103907

[62] [62] Q. Shi, Z. Sun, X. Le, J. Xie, C. Lee, Soft robotic perception system with ultrasonic auto-positioning and multimodal sensory intelligence. ACS Nano 17, 4985–4998 (2023). 10.1021/acsnano.2c12592

[63] [63] B. Shih, D. Shah, J. Li, T.G. Thuruthel, Y.-L. Park et al., Electronic skins and machine learning for intelligent soft robots. Sci. Robot. 5, eaaz9239 (2020). 10.1126/scirobotics.aaz9239

[64] [64] H. Wang, C. Ma, L. Zhou, A brief review of machine learning and its application. 2009 International Conference on Information Engineering and Computer Science. Wuhan, China. IEEE, 1–4 (2009).

[65] [65] D. Dhall, R. Kaur, M. Juneja, Machine learning: a review of the algorithms and its applications. Proceedings of ICRIC 2019. Cham: Springer, 47–63 (2020).

[66] [66] S. Ray, A quick review of machine learning algorithms. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). Faridabad, India. IEEE, 35–39 (2019).

[67] [67] D. Ciregan, U. Meier, J. Schmidhuber, Multi-column deep neural networks for image classification. 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA. IEEE, 3642–3649 (2012).

[68] [68] S. Ji, W. Xu, M. Yang, K. Yu, 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 221–231 (2013). 10.1109/TPAMI.2012.59

[69] [69] Q.V. Le, W.Y. Zou, S.Y. Yeung, A.Y. Ng, Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. CVPR. Colorado Springs, CO, USA. IEEE, 3361–3368 (2011).

[70] [70] G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen et al., Machine learning of molecular electronic properties in chemical compound space. New J. Phys. 15, 095003 (2013). 10.1088/1367-2630/15/9/095003

[71] [71] W. Samek, A. Binder, G. Montavon, S. Lapuschkin, K.-R. Müller, Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. 28, 2660–2673 (2017). 10.1109/TNNLS.2016.2599820

[72] [72] Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015). 10.1038/nature14539

[73] [73] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015). 10.1007/s11263-015-0816-y

[74] [74] V. Gulshan, L. Peng, M. Coram, M.C. Stumpe, D. Wu et al., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016). 10.1001/jama.2016.17216

[75] [75] A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter et al., Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). 10.1038/nature21056

[76] [76] R. Yamashita, M. Nishio, R.K.G. Do, K. Togashi, Convolutional neural networks: an overview and application in radiology. Insights Imag. 9, 611–629 (2018). 10.1007/s13244-018-0639-9

[77] [77] Z. Li, F. Liu, W. Yang, S. Peng, J. Zhou, A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33, 6999–7019 (2022). 10.1109/TNNLS.2021.3084827

[78] [78] H. Wu, D. Sheng, X. Liu, Y. Zhou, L. Dong et al., NIR induced self-healing polyurethane/polypyrrole nanocomposites. Polymer 189, 122181 (2020). 10.1016/j.polymer.2020.122181

[79] [79] M. Yamamoto, J.L. White, Theory of deformation and strain-induced crystallization of an elastomeric network polymer. J. Polym. Sci. A-2 Polym. Phys. 9, 1399–1415 (1971). 10.1002/pol.1971.160090804

[80] [80] A. Poitou, A. Ammar, Y. Marco, L. Chevalier, M. Chaouche, Crystallization of polymers under strain: from molecular properties to macroscopic models. Comput. Meth. Appl. Mech. Eng. 192, 3245–3264 (2003). 10.1016/S0045-7825(03)00349-9

[81] [81] Y. Nie, G. Huang, L. Qu, X. Wang, G. Weng et al., New insights into thermodynamic description of strain-induced crystallization of peroxide cross-linked natural rubber filled with clay by tube model. Polymer 52, 3234–3242 (2011). 10.1016/j.polymer.2011.05.004

[82] [82] M. Tosaka, A route for the thermodynamic description of strain-induced crystallization in sulfur-cured natural rubber. Macromolecules 42, 6166–6174 (2009). 10.1021/ma900954c

[83] [83] Y. Nie, Thermodynamic description of strain-induced crystallization of natural rubber by a combination of the tube model and a scaling argument. J. Macromol. Sci. Part B 54, 492–506 (2015). 10.1080/00222348.2015.1019959

[84] [84] Y. Nie, H. Gao, Y. Wu, W. Hu, Thermodynamics of strain-induced crystallization of random copolymers. Soft Matter 10, 343–347 (2014). 10.1039/C3SM52465E

[85] [85] S.K. Patel, S. Malone, C. Cohen, J.R. Gillmor, R.H. Colby, Elastic modulus and equilibrium swelling of poly(dimethylsiloxane) networks. Macromolecules 25, 5241–5251 (1992). 10.1021/ma00046a021

[86] [86] L. Ionov, Polymeric actuators. Langmuir 31(18), 5015–5024 (2015). 10.1021/la503407z

[87] [87] H. Shigemune, S. Sugano, J. Nishitani, M. Yamauchi, N. Hosoya et al., Dielectric elastomer actuators with carbon nanotube electrodes painted with a soft brush. Actuators 7, 51 (2018). 10.3390/act7030051

[88] [88] N. Wang, C. Cui, H. Guo, B. Chen, X. Zhang, Advances in dielectric elastomer actuation technology. Sci. China Technol. Sci. 61, 1512–1527 (2018). 10.1007/s11431-017-9140-0

[89] [89] Q.M. Zhang, M.J. Serpe, Stimuli-responsive polymers for actuation. ChemPhysChem 18, 1451–1465 (2017). 10.1002/cphc.201601187

[90] [90] U. Gupta, L. Qin, Y. Wang, H. Godaba, J. Zhu, Soft robots based on dielectric elastomer actuators: a review. Smart Mater. Struct. 28, 103002 (2019). 10.1088/1361-665x/ab3a77

[91] [91] O. Felfoul, M. Mohammadi, S. Taherkhani, D. de Lanauze, Z.X. Yong et al., Magneto-aerotactic bacteria deliver drug-containing nanoliposomes to tumour hypoxic regions. Nat. Nanotechnol. 11, 941–947 (2016). 10.1038/nnano.2016.137

[92] [92] D. Yang, B. Lu, Y. Zhao, X. Jiang, Fabrication of aligned fibrous arrays by magnetic electrospinning. Adv. Mater. 19(21), 3702–3706 (2007). 10.1002/adma.200700171

[93] [93] X. Wang, G. Mao, J. Ge, M. Drack, G.S. Cañón Bermúdez et al., Untethered and ultrafast soft-bodied robots. Commun. Mater. 1, 67 (2020). 10.1038/s43246-020-00067-1

[94] [94] J. Yunas, B. Mulyanti, I. Hamidah, M. Mohd Said, R.E. Pawinanto et al., Polymer-based MEMS electromagnetic actuator for biomedical application: a review. Polymers 12, 1184 (2020). 10.3390/polym12051184

[95] [95] M. Li, Y. Wang, A. Chen, A. Naidu, B.S. Napier et al., Flexible magnetic composites for light-controlled actuation and interfaces. Proc. Natl. Acad. Sci. U.S.A. 115, 8119–8124 (2018). 10.1073/pnas.1805832115

[96] [96] J. Kim, J.W. Kim, H.C. Kim, L. Zhai, H.-U. Ko et al., Review of soft actuator materials. Int. J. Precis. Eng. Manuf. 20, 2221–2241 (2019). 10.1007/s12541-019-00255-1

[97] [97] A. Vintiloiu, J.-C. Leroux, Organogels and their use in drug delivery: a review. J. Control. Release 125, 179–192 (2008). 10.1016/j.jconrel.2007.09.014

[98] [98] G.V. Stoychev, L. Ionov, Actuating fibers: design and applications. ACS Appl. Mater. Interfaces 8, 24281–24294 (2016). 10.1021/acsami.6b07374

[99] [99] T.S. Shim, J.M. Kim, Soft-, shape changing materials toward physicochemically powered actuators. Korean J. Chem. Eng. 34, 2355–2365 (2017). 10.1007/s11814-017-0199-6

[100] [100] P.J. Flory, Thermodynamics of high polymer solutions. J. Chem. Phys. 9, 660 (1941). 10.1063/1.1750971

[101] [101] A.R. Khokhlov, P.G. Khalatur, Conformation-dependent sequence design (engineering) of AB copolymers. Phys. Rev. Lett. 82, 3456–3459 (1999). 10.1103/physrevlett.82.3456

[102] [102] M. Quesada-Pérez, J.A. Maroto-Centeno, J. Forcada, R. Hidalgo-Alvarez, Gel swelling theories: the classical formalism and recent approaches. Soft Matter 7, 10536–10547 (2011). 10.1039/C1SM06031G

[103] [103] M.A. Mohamed, A. Fallahi, A.M.A. El-Sokkary, S. Salehi, M.A. Akl et al., Stimuli-responsive hydrogels for manipulation of cell microenvironment: from chemistry to biofabrication technology. Prog. Polym. Sci. 98, 101147 (2019). 10.1016/j.progpolymsci.2019.101147

[104] [104] C. Löwenberg, M. Balk, C. Wischke, M. Behl, A. Lendlein, Shape-memory hydrogels: evolution of structural principles to enable shape switching of hydrophilic polymer networks. Acc. Chem. Res. 50, 723–732 (2017). 10.1021/acs.accounts.6b00584

[105] [105] Y. Han, T. Bai, W. Liu, Controlled heterogeneous stem cell differentiation on a shape memory hydrogel surface. Sci. Rep. 4, 5815 (2014). 10.1038/srep05815

[106] [106] A. Kirillova, L. Ionov, Shape-changing polymers for biomedical applications. J. Mater. Chem. B 7, 1597–1624 (2019). 10.1039/c8tb02579g

[107] [107] Y. Xia, Y. He, F. Zhang, Y. Liu, J. Leng, A review of shape memory polymers and composites: mechanisms, materials, and applications. Adv. Mater. 33, 2000713 (2021). 10.1002/adma.202000713

[108] [108] R.M. Van Horn, M.R. Steffen, D. O’Connor, Recent progress in block copolymer crystallization. Polym. Cryst. 1, e10039 (2018). 10.1002/pcr2.10039

[109] [109] M. Bothe, F. Emmerling, T. Pretsch, Poly(ester urethane) with varying polyester chain length: polymorphism and shape-memory behavior. Macromol. Chem. Phys. 214, 2683–2693 (2013). 10.1002/macp.201300464

[110] [110] T. van Manen, S. Janbaz, A.A. Zadpoor, Programming 2D/3D shape-shifting with hobbyist 3D printers. Mater. Horiz. 4, 1064–1069 (2017). 10.1039/c7mh00269f

[111] [111] G. Stoychev, A. Kirillova, L. Ionov, Light-responsive shape-changing polymers. Adv. Opt. Mater. 7, 1900067 (2019). 10.1002/adom.201900067

[112] [112] R.S. Kularatne, H. Kim, J.M. Boothby, T.H. Ware, Liquid crystal elastomer actuators: Synthesis, alignment, and applications. J. Polym. Sci. Part B Polym. Phys. 55, 395–411 (2017). 10.1002/polb.24287

[113] [113] H. Jiang, C. Li, X. Huang, Actuators based on liquid crystalline elastomer materials. Nanoscale 5, 5225 (2013). 10.1039/c3nr00037k

[114] [114] J. Ahn, Y. Jeong, Z.-J. Zhao, S. Hwang, K. Kim et al., Heterogeneous conductance-based locally shape-morphable soft electrothermal actuator. Adv. Mater. Technol. 5, 1900997 (2020). 10.1002/admt.201900997

[115] [115] Y. Hu, J. Liu, L. Chang, L. Yang, A. Xu et al., Actuators: electrically and sunlight-driven actuator with versatile biomimetic motions based on rolled carbon nanotube bilayer composite. Adv. Funct. Mater. 27, 1770265 (2017). 10.1002/adfm.201770265

[116] [116] H. Hu, M. Nie, M. Galluzzi, X. Yu, X. Du, Mimosa-inspired high-sensitive and multi-responsive starch actuators. Adv. Funct. Mater. 33, 2304634 (2023). 10.1002/adfm.202304634

[117] [117] L. Tian, B. Zimmerman, A. Akhtar, K.J. Yu, M. Moore et al., Large-area MRI-compatible epidermal electronic interfaces for prosthetic control and cognitive monitoring. Nat. Biomed. Eng. 3, 194–205 (2019). 10.1038/s41551-019-0347-x

[118] [118] A. Chortos, J. Liu, Z. Bao, Pursuing prosthetic electronic skin. Nat. Mater. 15, 937–950 (2016). 10.1038/nmat4671

[119] [119] P. Delmas, J. Hao, L. Rodat-Despoix, Molecular mechanisms of mechanotransduction in mammalian sensory neurons. Nat. Rev. Neurosci. 12, 139–153 (2011). 10.1038/nrn2993

[120] [120] Y.C. Lai, J. Deng, R. Liu, Y.C. Hsiao, S.L. Zhang et al., Actively perceiving and responsive soft robots enabled by self-powered, highly extensible, and highly sensitive triboelectric proximity- and pressure-sensing skins. Adv. Mater. 30, e1801114 (2018). 10.1002/adma.201801114

[121] [121] Z. Wang, L. Wang, Y. Wu, L. Bian, M. Nagai et al., Signal filtering enabled by spike voltage-dependent plasticity in metalloporphyrin-based memristors. Adv. Mater. 33, e2104370 (2021). 10.1002/adma.202104370

[122] [122] F. Hartmann, M. Drack, M. Kaltenbrunner, Meant to merge: Fabrication of stretchy electronics for robotics. Sci. Robot. 3, eaat9091 (2018). 10.1126/scirobotics.aat9091

[123] [123] C. Wang, K. Sim, J. Chen, H. Kim, Z. Rao et al., Soft ultrathin electronics innervated adaptive fully soft robots. Adv. Mater. 30, e1706695 (2018). 10.1002/adma.201706695

[124] [124] Y. Lee, J.Y. Oh, W. Xu, O. Kim, T.R. Kim et al., Stretchable organic optoelectronic sensorimotor synapse. Sci. Adv. 4, eaat7387 (2018). 10.1126/sciadv.aat7387

[125] [125] Y. Yang, Y. Song, X. Bo, J. Min, O.S. Pak et al., A laser-engraved wearable sensor for sensitive detection of uric acid and tyrosine in sweat. Nat. Biotechnol. 38, 217–224 (2020). 10.1038/s41587-019-0321-x

[126] [126] Y. Wang, T. Tang, Y. Xu, Y. Bai, L. Yin et al., All-weather, natural silent speech recognition via machine-learning-assisted tattoo-like electronics. npj Flex. Electron. 5, 20 (2021). 10.1038/s41528-021-00119-7

[127] [127] M. Kaltenbrunner, T. Sekitani, J. Reeder, T. Yokota, K. Kuribara et al., An ultra-lightweight design for imperceptible plastic electronics. Nature 499, 458–463 (2013). 10.1038/nature12314

[128] [128] X. Ning, X. Yu, H. Wang, R. Sun, R.E. Corman et al., Mechanically active materials in three-dimensional mesostructures. Sci. Adv. 4, eaat8313 (2018). 10.1126/sciadv.aat8313

[129] [129] K. He, Y. Liu, M. Wang, G. Chen, Y. Jiang et al., An artificial somatic reflex arc. Adv. Mater. 32, e1905399 (2020). 10.1002/adma.201905399

[130] [130] R. Wang, Y. Shen, D. Qian, J. Sun, X. Zhou et al., Tensile and torsional elastomer fiber artificial muscle by entropic elasticity with thermo-piezoresistive sensing of strain and rotation by a single electric signal. Mater. Horiz. 7, 3305–3315 (2020). 10.1039/D0MH01003K

[131] [131] D. Tan, B. Xu, Advanced interfacial design for electronic skins with customizable functionalities and wearability. Adv. Funct. Mater. 33, 2306793 (2023). 10.1002/adfm.202306793

[132] [132] Y. Qiu, S. Sun, X. Wang, K. Shi, Z. Wang et al., Nondestructive identification of softness via bioinspired multisensory electronic skins integrated on a robotic hand. npj Flex. Electron. 6, 45 (2022). 10.1038/s41528-022-00181-9

[133] [133] K.K. Kim, M. Kim, K. Pyun, J. Kim, J. Min et al., A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition. Nat. Electron. 6, 64–75 (2023). 10.1038/s41928-022-00888-7

[134] [134] K.R. Pyun, K. Kwon, M.J. Yoo, K.K. Kim, D. Gong et al., Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications. Natl. Sci. Rev. 11, nwad298 (2023). 10.1093/nsr/nwad298

[135] [135] K.K. Kim, I. Ha, M. Kim, J. Choi, P. Won et al., A deep-learned skin sensor decoding the epicentral human motions. Nat. Commun. 11, 2149 (2020). 10.1038/s41467-020-16040-y

[136] [136] D. Kim, J. Min, S.H. Ko, Recent developments and future directions of wearable skin biosignal sensors. Adv. Sens. Res. 3, 2300118 (2024). 10.1002/adsr.202300118

[137] [137] S. Gong, W. Schwalb, Y. Wang, Y. Chen, Y. Tang et al., A wearable and highly sensitive pressure sensor with ultrathin gold nanowires. Nat. Commun. 5, 3132 (2014). 10.1038/ncomms4132

[138] [138] Y. Cao, T. Li, Y. Gu, H. Luo, S. Wang et al., Fingerprint-inspired flexible tactile sensor for accurately discerning surface texture. Small 14, e1703902 (2018). 10.1002/smll.201703902

[139] [139] H.L. Wang, S.Y. Kuang, H.Y. Li, Z.L. Wang, G. Zhu, Large-area integrated triboelectric sensor array for wireless static and dynamic pressure detection and mapping. Small 16, e1906352 (2020). 10.1002/smll.201906352

[140] [140] X. Zhao, Z. Zhang, L. Xu, F. Gao, B. Zhao et al., Fingerprint-inspired electronic skin based on triboelectric nanogenerator for fine texture recognition. Nano Energy 85, 106001 (2021). 10.1016/j.nanoen.2021.106001

[141] [141] Q. Hua, J. Sun, H. Liu, R. Bao, R. Yu et al., Skin-inspired highly stretchable and conformable matrix networks for multifunctional sensing. Nat. Commun. 9, 244 (2018). 10.1038/s41467-017-02685-9

[142] [142] Z. Huang, Y. Hao, Y. Li, H. Hu, C. Wang et al., Three-dimensional integrated stretchable electronics. Nat. Electron. 1, 473–480 (2018). 10.1038/s41928-018-0116-y

[143] [143] C.M. Boutry, M. Negre, M. Jorda, O. Vardoulis, A. Chortos et al., A hierarchically patterned, bioinspired e-skin able to detect the direction of applied pressure for robotics. Sci. Robot. 3, eaau6914 (2018). 10.1126/scirobotics.aau6914

[144] [144] J.C. Yang, J. Mun, S.Y. Kwon, S. Park, Z. Bao et al., Electronic skin: recent progress and future prospects for skin-attachable devices for health monitoring, robotics, and prosthetics. Adv. Mater. 31, e1904765 (2019). 10.1002/adma.201904765

[145] [145] Y. Lee, J.-H. Ahn, Biomimetic tactile sensors based on nanomaterials. ACS Nano 14, 1220–1226 (2020). 10.1021/acsnano.0c00363

[146] [146] D.H. Ho, Q. Sun, S.Y. Kim, J.T. Han, D.H. Kim et al., Stretchable and multimodal all graphene electronic skin. Adv. Mater. 28, 2601–2608 (2016). 10.1002/adma.201505739

[147] [147] D. Kong, G. Yang, G. Pang, Z. Ye, H. Lv et al., Bioinspired co-design of tactile sensor and deep learning algorithm for human–robot interaction. Adv. Intell. Systems 4(6), 2200050 (2022). 10.1002/aisy.202200050

[148] [148] M. Zhu, Z. Sun, Z. Zhang, Q. Shi, T. He et al., Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications. Sci. Adv. 6, eaaz8693 (2020). 10.1126/sciadv.aaz8693

[149] [149] C. Dhong, R. Miller, N.B. Root, S. Gupta, L.V. Kayser et al., Role of indentation depth and contact area on human perception of softness for haptic interfaces. Sci. Adv. 5, eaaz8845 (2019). 10.1126/sciadv.aaw8845

[150] [150] H.P. Saal, B.P. Delhaye, B.C. Rayhaun, S.J. Bensmaia, Simulating tactile signals from the whole hand with millisecond precision. Proc. Natl. Acad. Sci. U.S.A. 114, E5693–E5702 (2017). 10.1073/pnas.1704856114

[151] [151] G.H. Lee, J.K. Park, J. Byun, J.C. Yang, S.Y. Kwon et al., Parallel signal processing of a wireless pressure-sensing platform combined with machine-learning-based cognition, inspired by the human somatosensory system. Adv. Mater. 32, e1906269 (2020). 10.1002/adma.201906269

[152] [152] J. Lee, Y. Yoon, H. Park, J. Choi, Y. Jung et al., Bioinspired soft robotic fish for wireless underwater control of gliding locomotion. Adv. Intell. Syst. 4, 2270033 (2022). 10.1002/aisy.202270033

[153] [153] Y. Yoon, H. Park, J. Lee, J. Choi, Y. Jung et al., Bioinspired untethered soft robot with pumpless phase change soft actuators by bidirectional thermoelectrics. Chem. Eng. J. 451, 138794 (2023). 10.1016/j.cej.2022.138794

[154] [154] M. De Volder, D. Reynaerts, Pneumatic and hydraulic microactuators: a review. J. Micromech. Microeng. 20, 043001 (2010). 10.1088/0960-1317/20/4/043001

[155] [155] H. Lee, H. Kim, I. Ha, J. Jung, P. Won et al., Directional shape morphing transparent walking soft robot. Soft Robot. 6, 760–767 (2019). 10.1089/soro.2018.0164

[156] [156] P. Won, K.K. Kim, H. Kim, J.J. Park, I. Ha et al., Transparent soft actuators/sensors and camouflage skins for imperceptible soft robotics. Adv. Mater. 33, e2002397 (2021). 10.1002/adma.202002397

[157] [157] H. Kim, S.-K. Ahn, D.M. Mackie, J. Kwon, S.H. Kim et al., Shape morphing smart 3D actuator materials for micro soft robot. Mater. Today 41, 243–269 (2020). 10.1016/j.mattod.2020.06.005

[158] [158] D. Won, H. Cho, H. Kim, G. Lee, J. Kwon et al., Digital selective reversible phase control of monolithically integrated heterogeneous piezoelectric polymer for frequency dependent unimorph. Adv. Opt. Mater. 10, 2201206 (2022). 10.1002/adom.202201206

[159] [159] D.P. Arnold, N. Wang, Permanent magnets for mems. J. Microelectromech. Syst. 18, 1255–1266 (2009). 10.1109/JMEMS.2009.2034389

[160] [160] A. Rafsanjani, Y. Zhang, B. Liu, S.M. Rubinstein, K. Bertoldi, Kirigami skins make a simple soft actuator crawl. Sci. Robot. 3, eaar7555 (2018). 10.1126/scirobotics.aar7555

[161] [161] J.H. Pikul, S. Li, H. Bai, R.T. Hanlon, I. Cohen et al., Stretchable surfaces with programmable 3D texture morphing for synthetic camouflaging skins. Science 358, 210–214 (2017). 10.1126/science.aan5627

[162] [162] F. Connolly, P. Polygerinos, C.J. Walsh, K. Bertoldi, Mechanical programming of soft actuators by varying fiber angle. Soft Robot. 2, 26–32 (2015). 10.1089/soro.2015.0001

[163] [163] T. Umedachi, V. Vikas, B.A. Trimmer, Softworms: the design and control of non-pneumatic, 3D-printed, deformable robots. Bioinspir. Biomim. 11, 025001 (2016). 10.1088/1748-3190/11/2/025001

[164] [164] S. Shian, K. Bertoldi, D.R. Clarke, Dielectric elastomer based “grippers” for soft robotics. Adv. Mater. 27, 6814–6819 (2015). 10.1002/adma.201503078

[165] [165] T. Li, G. Li, Y. Liang, T. Cheng, J. Dai et al., Fast-moving soft electronic fish. Sci. Adv. 3, e1602045 (2017). 10.1126/sciadv.1602045

[166] [166] H. Yuk, S. Lin, C. Ma, M. Takaffoli, N.X. Fang et al., Hydraulic hydrogel actuators and robots optically and sonically camouflaged in water. Nat. Commun. 8, 14230 (2017). 10.1038/ncomms14230

[167] [167] N. Bandari, J. Dargahi, M. Packirisamy, Tactile sensors for minimally invasive surgery: a review of the state-of-the-art, applications, and perspectives. IEEE Access 8, 7682–7708 (2019). 10.1109/ACCESS.2019.2962636

[168] [168] B. Dong, Q. Shi, T. He, S. Zhu, Z. Zhang et al., Wearable triboelectric/aluminum nitride nano-energy-nano-system with self-sustainable photonic modulation and continuous force sensing. Adv. Sci. 7, 1903636 (2020). 10.1002/advs.201903636

[169] [169] J.Z. Gul, M. Sajid, K.H. Choi, Retracted article: 3D printed highly flexible strain sensor based on TPU–graphene composite for feedback from high speed robotic applications. J. Mater. Chem. C 7, 4692–4701 (2019). 10.1039/C8TC03423K

[170] [170] E. Fujiwara, M.F.M. dos Santos, C.K. Suzuki, Flexible optical fiber bending transducer for application in glove-based sensors. IEEE Sens. J. 14, 3631–3636 (2014). 10.1109/JSEN.2014.2330998

[171] [171] B. Dahroug, B. Tamadazte, S. Weber, L. Tavernier, N. Andreff, Review on otological robotic systems: toward microrobot-assisted cholesteatoma surgery. IEEE Rev. Biomed. Eng. 11, 125–142 (2018). 10.1109/RBME.2018.2810605

[172] [172] S. Lee, Q. Shi, C. Lee, From flexible electronics technology in the era of IoT and artificial intelligence toward future implanted body sensor networks. APL Mater. 7, 031302 (2019). 10.1063/1.5063498

[173] [173] M. Zhu, T. He, C. Lee, Technologies toward next generation human machine interfaces: from machine learning enhanced tactile sensing to neuromorphic sensory systems. Appl. Phys. Rev. 7, 031305 (2020). 10.1063/5.0016485

[174] [174] R. Krishnamurthy, J. Cecil, A next-generation IoT-based collaborative framework for electronics assembly. Int. J. Adv. Manuf. Technol. 96, 39–52 (2018). 10.1007/s00170-017-1561-x

[175] [175] C. Zhuang, J. Liu, H. Xiong, Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int. J. Adv. Manuf. Technol. 96, 1149–1163 (2018). 10.1007/s00170-018-1617-6

[176] [176] M. Haras, T. Skotnicki, Thermoelectricity for IoT–a review. Nano Energy 54, 461–476 (2018). 10.1016/j.nanoen.2018.10.013

[177] [177] Y. Liu, C. Yiu, Z. Song, Y. Huang, K. Yao et al., Electronic skin as wireless human-machine interfaces for robotic VR. Sci. Adv. 8, eabl700 (2022). 10.1126/sciadv.abl6700

[178] [178] M. Cai, Q. Wang, Z. Qi, D. Jin, X. Wu et al., Deep reinforcement learning framework-based flow rate rejection control of soft magnetic miniature robots. IEEE Trans. Cybern. 53, 7699–7711 (2023). 10.1109/TCYB.2022.3199213

[179] [179] P.E. Dupont, B.J. Nelson, M. Goldfarb, B. Hannaford, A. Menciassi et al., A decade retrospective of medical robotics research from 2010 to 2020. Sci. Robot. 6, eabi8017 (2021). 10.1126/scirobotics.abi8017

[180] [180] W. Hu, G.Z. Lum, M. Mastrangeli, M. Sitti, Small-scale soft-bodied robot with multimodal locomotion. Nature 554, 81–85 (2018). 10.1038/nature25443

[181] [181] B.J. Nelson, I.K. Kaliakatsos, J.J. Abbott, Microrobots for minimally invasive medicine. Annu. Rev. Biomed. Eng. 12, 55–85 (2010). 10.1146/annurev-bioeng-010510-103409

[182] [182] T. Xu, C. Huang, Z. Lai, X. Wu, Independent control strategy of multiple magnetic flexible millirobots for position control and path following. IEEE Trans. Robot. 38, 2875–2887 (2022). 10.1109/TRO.2022.3157147

[183] [183] Q. Wang, Y. Tian, X. Du, H. Ko, B.Y.M. Ip et al., Magnetic navigation of collective cell microrobots in blood under ultrasound Doppler imaging. IEEE/ASME Trans. Mechatron. 27, 3174–3185 (2022). 10.1109/TMECH.2021.3109346

[184] [184] J. Nam, W. Lee, J. Kim, G. Jang, Magnetic helical robot for targeted drug-delivery in tubular environments. IEEE/ASME Trans. Mechatron. 22, 2461–2468 (2017). 10.1109/TMECH.2017.2761786

[185] [185] K.T. Nguyen, G. Go, Z. Jin, B.A. Darmawan, A. Yoo et al., A magnetically guided self-rolled microrobot for targeted drug delivery, real-time X-ray imaging, and microrobot retrieval. Adv. Healthc. Mater. 10, e2001681 (2021). 10.1002/adhm.202001681

[186] [186] Z. Wang, S. Guo, J. Guo, Q. Fu, L. Zheng et al., Selective motion control of a novel magnetic-driven minirobot with targeted drug sustained-release function. IEEE/ASME Trans. Mechatron. 27, 336–347 (2022). 10.1109/TMECH.2021.3063750

[187] [187] R. Yu, S.L. Charreyron, Q. Boehler, C. Weibel, C. Chautems et al., Modeling electromagnetic navigation systems for medical applications using random forests and artificial neural networks. 2020 IEEE International Conference on Robotics and Automation (ICRA). Paris, France. IEEE, 9251–9256 (2020).

[188] [188] T. Xu, Y. Guan, J. Liu, X. Wu, Image-based visual servoing of helical microswimmers for planar path following. IEEE Trans. Autom. Sci. Eng. 17, 325–333 (2020). 10.1109/TASE.2019.2911985

[189] [189] T. Xu, Z. Hao, C. Huang, J. Yu, L. Zhang et al., Multimodal locomotion control of needle-like microrobots assembled by ferromagnetic nanoparticles. IEEE/ASME Trans. Mechatron. 27, 4327–4338 (2022). 10.1109/TMECH.2022.3155806

[190] [190] J. Kim, H. Choi, J. Kim, A robust motion control with antiwindup scheme for electromagnetic actuated microrobot using time-delay estimation. IEEE/ASME Trans. Mechatron. 24, 1096–1105 (2019). 10.1109/TMECH.2019.2907145

[191] [191] W. He, T. Meng, X. He, C. Sun, Iterative learning control for a flapping wing micro aerial vehicle under distributed disturbances. IEEE Trans. Cybern. 49, 1524–1535 (2019). 10.1109/TCYB.2018.2808321

[192] [192] S. Xu, J. Liu, C. Yang, X. Wu, T. Xu, A learning-based stable servo control strategy using broad learning system applied for microrobotic control. IEEE Trans. Cybern. 52, 13727–13737 (2022). 10.1109/TCYB.2021.3121080

[193] [193] T.T. Nguyen, N.D. Nguyen, S. Nahavandi, Deep reinforcement learning for multiagent systems: a review of challenges, solutions, and applications. IEEE Trans. Cybern. 50, 3826–3839 (2020). 10.1109/TCYB.2020.2977374

[194] [194] L. Ren, Q. Wu, Q. Liu, P. Hao, J. Tang et al., Stiffness-tunable and self-sensing integrated soft machines based on 4D printed conductive shape memory composites. Mater. Des. 228, 111851 (2023). 10.1016/j.matdes.2023.111851

[195] [195] N. Vogt, Machine learning in neuroscience. Nat. Meth. 15, 33 (2018). 10.1038/nmeth.4549

[196] [196] A. Speiser, L.-R. Müller, P. Hoess, U. Matti, C.J. Obara et al., Deep learning enables fast and dense single-molecule localization with high accuracy. Nat. Methods 18, 1082–1090 (2021). 10.1038/s41592-021-01236-x

[197] [197] C. Belthangady, L.A. Royer, Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Meth. 16, 1215–1225 (2019). 10.1038/s41592-019-0458-z

[198] [198] M. Weigert, U. Schmidt, T. Boothe, A. Müller, A. Dibrov et al., Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15, 1090–1097 (2018). 10.1038/s41592-018-0216-7

[199] [199] E. Gómez-de-Mariscal, C. García-López-de-Haro, W. Ouyang, L. Donati, E. Lundberg et al., DeepImageJ: a user-friendly environment to run deep learning models in ImageJ. Nat. Methods 18, 1192–1195 (2021). 10.1038/s41592-021-01262-9

[200] [200] F.V. Lavrentev, I.S. Rumyantsev, A.S. Ivanov, V.V. Shilovskikh, O.Y. Orlova et al., Soft hydrogel actuator for fast machine-learning-assisted bacteria detection. ACS Appl. Mater. Interfaces 14, 7321–7328 (2022). 10.1021/acsami.1c22470

[201] [201] D. Rus, M.T. Tolley, Design, fabrication and control of origami robots. Nat. Rev. Mater. 3, 101–112 (2018). 10.1038/s41578-018-0009-8

[202] [202] A.J. Ijspeert, A. Crespi, D. Ryczko, J.M. Cabelguen, From swimming to walking with a salamander robot driven by a spinal cord model. Science 315, 1416–1420 (2007). 10.1126/science.1138353

[203] [203] J. Bongard, V. Zykov, H. Lipson, Resilient machines through continuous self-modeling. Science 314, 1118–1121 (2006). 10.1126/science.1133687

[204] [204] A. Cully, J. Clune, D. Tarapore, J.-B. Mouret, Robots that can adapt like animals. Nature 521, 503–507 (2015). 10.1038/nature14422

[205] [205] H. Hauser, Resilient machines through adaptive morphology. Nat. Mach. Intell. 1, 338–339 (2019). 10.1038/s42256-019-0076-6

[206] [206] D.-Y. Lee, S.-R. Kim, J.-S. Kim, J.-J. Park, K.-J. Cho, Origami wheel transformer: a variable-diameter wheel drive robot using an origami structure. Soft Robot. 4, 163–180 (2017). 10.1089/soro.2016.0038

[207] [207] J. Hiller, H. Lipson, Dynamic simulation of soft multimaterial 3D-printed objects. Soft Robot. 1, 88–101 (2014). 10.1089/soro.2013.0010

[208] [208] H. Lipson, J.B. Pollack, Automatic design and manufacture of robotic lifeforms. Nature 406, 974–978 (2000). 10.1038/35023115

[209] [209] Y. Huang, J. Zhou, P. Ke, X. Guo, C.K. Yiu et al., A skin-integrated multimodal haptic interface for immersive tactile feedback. Nat. Electron. 6, 1020–1031 (2023). 10.1038/s41928-023-01074-z

[210] [210] R.B.N. Scharff, G. Fang, Y. Tian, J. Wu, J.M.P. Geraedts et al., Sensing and reconstruction of 3-D deformation on pneumatic soft robots. IEEE/ASME Trans. Mechatron. 26, 1877–1885 (2021). 10.1109/TMECH.2021.3078263

[211] [211] G. Fang, Y. Tian, Z.-X. Yang, J.M.P. Geraedts, C.C.L. Wang, Efficient Jacobian-based inverse kinematics with sim-to-real transfer of soft robots by learning. IEEE/ASME Trans. Mechatron. 27, 5296–5306 (2022). 10.1109/TMECH.2022.3178303

[212] [212] Y.F. Zhang, N. Zhang, H. Hingorani, N. Ding, D. Wang et al., Fast-response, stiffness-tunable soft actuator by hybrid multimaterial 3D printing. Adv. Funct. Mater. 29, 1806698 (2019). 10.1002/adfm.201806698

[213] [213] B.W.K. Ang, C.H. Yeow, A learning-based approach to sensorize soft robots. Soft Robot. 9, 1144–1153 (2022). 10.1089/soro.2020.0172

[214] [214] G. Li, T. Stalin, V.T. Truong, P.V.Y. Alvarado, DNN-based predictive model for a batoid-inspired soft robot. IEEE Robot. Autom. Lett. 7, 1024–1031 (2022). 10.1109/LRA.2021.3135573

[215] [215] Y. Sun, Y. Ju, H. Wen, R. Liu, Q. Cao et al., Hybrid-excited magneto-responsive soft actuators for grasping and manipulation of objects. Appl. Mater. Today 35, 101917 (2023). 10.1016/j.apmt.2023.101917

[216] [216] P. Won, S.H. Ko, C. Majidi, A.W. Feinberg, V.A. Webster-Wood, Biohybrid actuators for soft robotics: challenges in scaling up. Actuators 9, 96 (2020). 10.3390/act9040096

[217] [217] Y. Roh, Y. Lee, D. Lim, D. Gong, S. Hwang et al., Nature’s blueprint in bioinspired materials for robotics. Adv. Funct. Mater. 2306079 (2023). 10.1002/adfm.202306079

[218] [218] H. Kim, H. Lee, I. Ha, J. Jung, P. Won et al., Biomimetic color changing anisotropic soft actuators with integrated metal nanowire percolation network transparent heaters for soft robotics. Adv. Funct. Mater. 28, 1801847 (2018). 10.1002/adfm.201801847

[219] [219] H.J. Lee, S. Baik, G.W. Hwang, J.H. Song, D.W. Kim et al., An electronically perceptive bioinspired soft wet-adhesion actuator with carbon nanotube-based strain sensors. ACS Nano 15, 14137–14148 (2021). 10.1021/acsnano.1c05130

[220] [220] W. Liu, Y. Duo, X. Chen, B. Chen, T. Bu et al., An intelligent robotic system capable of sensing and describing objects based on bimodal, self-powered flexible sensors. Adv. Funct. Mater. 33, 2306368 (2023). 10.1002/adfm.202306368

[221] [221] M.H. Syu, Y.J. Guan, W.C. Lo, Y.K. Fuh, Biomimetic and porous nanofiber-based hybrid sensor for multifunctional pressure sensing and human gesture identification via deep learning method. Nano Energy 76, 105029 (2020). 10.1016/j.nanoen.2020.105029

[222] [222] K. Tao, J. Yu, J. Zhang, A. Bao, H. Hu et al., Deep-learning enabled active biomimetic multifunctional hydrogel electronic skin. ACS Nano 17, 16160–16173 (2023). 10.1021/acsnano.3c05253

[223] [223] W. Xu, J. Cui, L. Li, B. Yao, S. Tian et al., Digital twin-based industrial cloud robotics: framework, control approach and implementation. J. Manuf. Syst. 58, 196–209 (2021). 10.1016/j.jmsy.2020.07.013

[224] [224] X. Yu, Z. Xie, Y. Yu, J. Lee, A. Vazquez-Guardado et al., Skin-integrated wireless haptic interfaces for virtual and augmented reality. Nature 575, 473–479 (2019). 10.1038/s41586-019-1687-0

[225] [225] J. Lee, D. Kim, H. Sul, S.H. Ko, Thermo-haptic materials and devices for wearable virtual and augmented reality. Adv. Funct. Mater. 31, 2007376 (2021). 10.1002/adfm.202007376

[226] [226] K.R. Pyun, J.A. Rogers, S.H. Ko, Materials and devices for immersive virtual reality. Nat. Rev. Mater. 7, 841–843 (2022). 10.1038/s41578-022-00501-5

[227] [227] J. Zeng, J. Zhao, T. Bu, G. Liu, Y. Qi et al., A flexible tribotronic artificial synapse with bioinspired neurosensory behavior. Nano-Micro Lett. 15, 18 (2022). 10.1007/s40820-022-00989-0

[228] [228] S.W. Cho, C. Jo, Y.-H. Kim, S.K. Park, Progress of materials and devices for neuromorphic vision sensors. Nano-Micro Lett. 14, 203 (2022). 10.1007/s40820-022-00945-y

[229] [229] R. Wu, S. Seo, L. Ma, J. Bae, T. Kim, Full-fiber auxetic-interlaced yarn sensor for sign-language translation glove assisted by artificial neural network. Nano-Micro Lett. 14, 139 (2022). 10.1007/s40820-022-00887-5

[230] [230] X. Chen, T. Wang, J. Shi, W. Lv, Y. Han et al., A novel artificial neuron-like gas sensor constructed from CuS quantum dots/Bi2S3 nanosheets. Nano-Micro Lett. 14, 8 (2021). 10.1007/s40820-021-00740-1

[231] [231] T. Sun, B. Feng, J. Huo, Y. Xiao, W. Wang et al., Artificial intelligence meets flexible sensors: emerging smart flexible sensing systems driven by machine learning and artificial synapses. Nano-Micro Lett. 16, 14 (2023). 10.1007/s40820-023-01235-x

[232] [232] Y. Wang, W. Gao, S. Yang, Q. Chen, C. Ye et al., Humanoid intelligent display platform for audiovisual interaction and sound identification. Nano-Micro Lett. 15, 221 (2023). 10.1007/s40820-023-01199-y

[233] [233] Y. Wang, M.L. Adam, Y. Zhao, W. Zheng, L. Gao et al., Machine learning-enhanced flexible mechanical sensing. Nano-Micro Lett. 15, 55 (2023). 10.1007/s40820-023-01013-9

[234] [234] Z. Shi, L. Meng, X. Shi, H. Li, J. Zhang et al., Morphological engineering of sensing materials for flexible pressure sensors and artificial intelligence applications. Nano-Micro Lett. 14, 141 (2022). 10.1007/s40820-022-00874-w

[235] [235] M. Zhu, Z. Sun, C. Lee, Soft modular glove with multimodal sensing and augmented haptic feedback enabled by materials’ multifunctionalities. ACS Nano 16, 14097–14110 (2022). 10.1021/acsnano.2c04043

[236] [236] J. Lee, H. Sul, W. Lee, K.R. Pyun, I. Ha et al., Stretchable skin-like cooling/heating device for reconstruction of artificial thermal sensation in virtual reality. Adv. Funct. Mater. 30, 1909171 (2020). 10.1002/adfm.201909171

[237] [237] Z. Sun, S. Wang, Y. Zhao, Z. Zhong, L. Zuo, Discriminating soft actuators’ thermal stimuli and mechanical deformation by hydrogel sensors and machine learning. Adv. Intell. Syst. 4, 2200089 (2022). 10.1002/aisy.202200089

[238] [238] Y. Jiang, J. An, F. Liang, G. Zuo, J. Yi et al., Knitted self-powered sensing textiles for machine learning-assisted sitting posture monitoring and correction. Nano Res. 15, 8389–8397 (2022). 10.1007/s12274-022-4409-0

[239] [239] M. Yang, F. Sun, X. Hu, F. Sun, Knitting from nature: self-sensing soft robotics enabled by all-in-one knit architectures. ACS Appl. Mater. Interfaces 15, 44294–44304 (2023). 10.1021/acsami.3c09029

[240] [240] M. Sun, B. Hao, S. Yang, X. Wang, C. Majidi et al., Exploiting ferrofluidic wetting for miniature soft machines. Nat. Commun. 13, 7919 (2022). 10.1038/s41467-022-35646-y

[241] [241] Z. Sun, M. Zhu, X. Shan, C. Lee, Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions. Nat. Commun. 13, 5224 (2022). 10.1038/s41467-022-32745-8

[242] [242] Y. Zhong, W. Tang, C. Zhang, Z. Jiao, D. Wu et al., Programmable thermochromic soft actuators with “two dimensional” bilayer architectures for soft robotics. Nano Energy 102, 107741 (2022). 10.1016/j.nanoen.2022.107741