{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T16:05:23Z","timestamp":1788451523768,"version":"build-2803163510"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Science and Engineering Research Council, Canada","award":["RGPIN 1512"],"award-info":[{"award-number":["RGPIN 1512"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Auton Robot"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10514-023-10130-8","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T15:02:11Z","timestamp":1694185331000},"page":"1195-1211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Sim-to-real transfer of co-optimized soft robot crawlers"],"prefix":"10.1007","volume":"47","author":[{"given":"Charles","family":"Schaff","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Audrey","family":"Sedal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyao","family":"Ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew R.","family":"Walter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"10130_CR1","doi-asserted-by":"crossref","unstructured":"Bern, J. M., Banzet, P., Poranne, R., et al. (2019). Trajectory optimization for cable-driven soft robot locomotion. In Proceedings of Robotics: Science and Systems (RSS).","DOI":"10.15607\/RSS.2019.XV.052"},{"key":"10130_CR2","doi-asserted-by":"crossref","unstructured":"Bern, J. M., Schnider, Y., Banzet, P., et al. (2020). Soft robot control with a learned differentiable model. In Proceedings of the IEEE International Conference on Soft Robotics (RoboSoft).","DOI":"10.1109\/RoboSoft48309.2020.9116011"},{"key":"10130_CR3","unstructured":"Bhatia, J., Jackson, H., Tian, Y., et al. (2021). Evolution gym: A large-scale benchmark for evolving soft robots. In Advances in Neural Information Processing Systems (NeurIPS)."},{"issue":"4","key":"10130_CR4","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1073\/pnas.1015390108","volume":"108","author":"J Bongard","year":"2011","unstructured":"Bongard, J. (2011). Morphological change in machines accelerates the evolution of robust behavior. Proceedings of the National Academy of Sciences, 108(4), 1234\u20131239.","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"2","key":"10130_CR5","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.1109\/LRA.2020.2969948","volume":"5","author":"G Bravo-Palacios","year":"2020","unstructured":"Bravo-Palacios, G., Del Prete, A., & Wensing, P. M. (2020). One robot for many tasks: Versatile co-design through stochastic programming. IEEE Robotics and Automation Letters, 5(2), 1680\u20131687.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10130_CR6","doi-asserted-by":"crossref","unstructured":"Bruder, D., Gillespie, B., Remy, C. D., et al. (2019). Modeling and control of soft robots using the Koopman operator and model predictive control. In Proceedings of Robotics: Science and Systems (RSS).","DOI":"10.15607\/RSS.2019.XV.060"},{"issue":"2","key":"10130_CR7","doi-asserted-by":"publisher","first-page":"4741","DOI":"10.1109\/LRA.2022.3146903","volume":"7","author":"A Centurelli","year":"2022","unstructured":"Centurelli, A., Arleo, L., Rizzo, A., et al. (2022). Closed-loop dynamic control of a soft manipulator using deep reinforcement learning. IEEE Robotics and Automation Letters, 7(2), 4741\u20134748.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10130_CR8","doi-asserted-by":"crossref","unstructured":"Chen, F., & Wang, M.Y. (2020). Design optimization of soft robots: A review of the state of the art. IEEE Robotics & Automation Magazine.","DOI":"10.1109\/MRA.2020.3024280"},{"key":"10130_CR9","unstructured":"Chen, T., He, Z., & Ciocarlie, M. (2020). Hardware as policy: Mechanical and computational co-optimization using deep reinforcement learning. arXiv:2008.04460."},{"key":"10130_CR10","doi-asserted-by":"crossref","unstructured":"Cheney, N., MacCurdy, R., Clune, J., et al. (2013). Unshackling evolution: Evolving soft robots with multiple materials and a powerful generative encoding. In Proceedings of the Annual Conference on Genetic and Evolutionary Computation (GECCO).","DOI":"10.1145\/2463372.2463404"},{"key":"10130_CR11","doi-asserted-by":"crossref","unstructured":"Coevoet, E., Morales-Bieze, T., Largilliere, F., et al. (2017). Software toolkit for modeling, simulation, and control of soft robots. Advanced Robotics 31.","DOI":"10.1080\/01691864.2017.1395362"},{"key":"10130_CR12","doi-asserted-by":"crossref","unstructured":"Collins, J., Chand, S., Vanderkop, A., et al. (2021). A review of physics simulators for robotic applications. IEEE Access.","DOI":"10.1109\/ACCESS.2021.3068769"},{"key":"10130_CR13","doi-asserted-by":"crossref","unstructured":"Culha, U., Demir, S. O., Trimpe, S., et al. (2020). Learning of sub-optimal gait controllers for magnetic walking soft millirobots. In Proceedings of Robotics: Science and Systems (RSS).","DOI":"10.15607\/RSS.2020.XVI.070"},{"key":"10130_CR14","doi-asserted-by":"crossref","unstructured":"Deimel, R., Irmisch, P., Wall, V., et al. (2017). Automated co-design of soft hand morphology and control strategy for grasping. In Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS).","DOI":"10.1109\/IROS.2017.8202294"},{"key":"10130_CR15","doi-asserted-by":"crossref","unstructured":"Digumarti, K. M., Gehring, C., Coros, S., et al. (2014). Concurrent optimization of mechanical design and locomotion control of a legged robot. In Mobile Service Robotics. p 315\u2013323.","DOI":"10.1142\/9789814623353_0037"},{"key":"10130_CR16","doi-asserted-by":"crossref","unstructured":"Drotman, D., Jadhav, S., Sharp, D., et al. (2021). Electronics-free pneumatic circuits for controlling soft-legged robots. Science Robotics 6(51).","DOI":"10.1126\/scirobotics.aay2627"},{"issue":"2","key":"10130_CR17","doi-asserted-by":"publisher","first-page":"5015","DOI":"10.1109\/LRA.2022.3154050","volume":"7","author":"M Dubied","year":"2022","unstructured":"Dubied, M., Michelis, M. Y., Spielberg, A., et al. (2022). Sim-to-real for soft robots using differentiable fem: Recipes for meshing, damping, and actuation. IEEE Robotics and Automation Letters, 7(2), 5015\u20135022.","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"9","key":"10130_CR18","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1002\/nme.4668","volume":"98","author":"C Farhat","year":"2014","unstructured":"Farhat, C., Avery, P., Chapman, T., et al. (2014). Dimensional reduction of nonlinear finite element dynamic models with finite rotations and energy-based mesh sampling and weighting for computational efficiency. International Journal for Numerical Methods in Engineering, 98(9), 625\u2013662.","journal-title":"International Journal for Numerical Methods in Engineering"},{"key":"10130_CR19","doi-asserted-by":"crossref","unstructured":"Faure, F., Duriez, C., Delingette, H., et al. (2012). SOFA: A multi-model framework for interactive physical simulation. In Soft Tissue Biomechanical Modeling for Computer Assisted Surgery. Studies in Mechanobiology, Tissue Engineering and Biomaterials, p 283\u2013321.","DOI":"10.1007\/8415_2012_125"},{"issue":"2","key":"10130_CR20","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1109\/LRA.2020.2966407","volume":"5","author":"B Gamus","year":"2020","unstructured":"Gamus, B., Salem, L., Gat, A. D., et al. (2020). Understanding inchworm crawling for soft-robotics. IEEE Robotics and Automation Letters, 5(2), 1397\u20131404.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10130_CR21","doi-asserted-by":"crossref","unstructured":"Geilinger, M., Poranne, R., Desai, R., et al. (2018). Skaterbots: Optimization-based design and motion synthesis for robotic creatures with legs and wheels. ACM Transactions on Graphics (TOG) 37(4).","DOI":"10.1145\/3197517.3201368"},{"issue":"6","key":"10130_CR22","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1109\/TRO.2018.2861900","volume":"34","author":"O Goury","year":"2018","unstructured":"Goury, O., & Duriez, C. (2018). Fast, generic, and reliable control and simulation of soft robots using model order reduction. Transactions on Robotics, 34(6), 1565\u20131576.","journal-title":"Transactions on Robotics"},{"issue":"4","key":"10130_CR23","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1162\/artl_a_00301","volume":"25","author":"D Ha","year":"2019","unstructured":"Ha, D. (2019). Reinforcement learning for improving agent design. Artificial Life, 25(4), 352\u2013365.","journal-title":"Artificial Life"},{"key":"10130_CR24","doi-asserted-by":"crossref","unstructured":"Ha, S., Coros, S., Alspach, A., et al. (2017). Joint optimization of robot design and motion parameters using the implicit function theorem. In Proceedings of Robotics: Science and Systems (RSS).","DOI":"10.15607\/RSS.2017.XIII.003"},{"key":"10130_CR25","unstructured":"Haarnoja, T., Zhou, A., Abbeel, P., et al. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proceedings of the International Conference on Machine Learning (ICML)."},{"key":"10130_CR26","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1089\/soro.2013.0010","volume":"1","author":"J Hiller","year":"2014","unstructured":"Hiller, J., & Lipson, H. (2014). Dynamic simulation of soft multimaterial 3D-printed objects. Soft Robotics, 1, 88\u2013101.","journal-title":"Soft Robotics"},{"key":"10130_CR27","doi-asserted-by":"crossref","unstructured":"Howison, T., Hauser, S., Hughes, J., et al. (2020). Reality-assisted evolution of soft robots through large-scale physical experimentation: A review. arXiv:2009.13960.","DOI":"10.1162\/artl_a_00330"},{"key":"10130_CR28","doi-asserted-by":"crossref","unstructured":"Hu, Y., Liu, J., Spielberg, A., et al. (2019). ChainQueen: A real-time differentiable physical simulator for soft robotics. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).","DOI":"10.1109\/ICRA.2019.8794333"},{"key":"10130_CR29","unstructured":"Huang, W., Mordatch, I., & Pathak, D. (2020). One policy to control them all: Shared modular policies for agent-agnostic control. arXiv:2007.04976."},{"key":"10130_CR30","doi-asserted-by":"crossref","unstructured":"Ju, H., Juan, R., Gomez, R., et al. (2022). Transferring policy of deep reinforcement learning from simulation to reality for robotics. Nature Machine Intelligence pp 1\u201311.","DOI":"10.1038\/s42256-022-00573-6"},{"key":"10130_CR31","doi-asserted-by":"crossref","unstructured":"Kim, D., Kim, S. H., Kim, T., et al. (2021). Review of machine learning methods in soft robotics. PLOS ONE 16(2).","DOI":"10.1371\/journal.pone.0246102"},{"key":"10130_CR32","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s43154-020-00021-6","volume":"1","author":"K Kleeberger","year":"2020","unstructured":"Kleeberger, K., Bormann, R., Kraus, W., et al. (2020). A survey on learning-based robotic grasping. Current Robotics Reports, 1, 239\u2013249.","journal-title":"Current Robotics Reports"},{"key":"10130_CR33","doi-asserted-by":"crossref","unstructured":"Kriegman, S., Walker, S., Shah, D., et al. (2019). Automated shapeshifting for function recovery in damaged robots. In Proceedings of Robotics: Science and Systems (RSS).","DOI":"10.15607\/RSS.2019.XV.028"},{"key":"10130_CR34","doi-asserted-by":"crossref","unstructured":"Kriegman, S., Nasab, A. M., Shah, D., et al. (2020). Scalable sim-to-real transfer of soft robot designs. In Proceedings of the IEEE International Conference on Soft Robotics (RoboSoft)","DOI":"10.1109\/RoboSoft48309.2020.9116004"},{"key":"10130_CR35","unstructured":"Kurin, V., Igl, M., Rockt\u00e4schel, T., et al. (2021). My body is a cage: The role of morphology in graph-based incompatible control. arXiv:2010.01856."},{"key":"10130_CR36","doi-asserted-by":"crossref","unstructured":"Lee, K., Kim, S., Lim, S., et al. (2020). Generalized Tsallis entropy reinforcement learning and its application to soft mobile robots. In Proceedings of Robotics: Science and Systems (RSS).","DOI":"10.15607\/RSS.2020.XVI.036"},{"key":"10130_CR37","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, X., & Kwok, K. W. (2022). Towards adaptive continuous control of soft robotic manipulator using reinforcement learning. In 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp 7074\u20137081.","DOI":"10.1109\/IROS47612.2022.9981335"},{"key":"10130_CR38","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1038\/35023115","volume":"406","author":"H Lipson","year":"2000","unstructured":"Lipson, H., & Pollack, J. B. (2000). Automatic design and manufacture of robotic lifeforms. Nature, 406, 974\u2013978.","journal-title":"Nature"},{"issue":"6389","key":"10130_CR39","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1126\/science.aar4586","volume":"360","author":"JI Lipton","year":"2018","unstructured":"Lipton, J. I., MacCurdy, R., Manchester, Z., et al. (2018). Handedness in shearing auxetics creates rigid and compliant structures. Science, 360(6389), 632\u2013635.","journal-title":"Science"},{"key":"10130_CR40","doi-asserted-by":"crossref","unstructured":"Luo, J., & Hauser, K. (2015). Robust trajectory optimization under frictional contact with iterative learning. In Proceedings of Robotics: Science and Systems (RSS).","DOI":"10.15607\/RSS.2015.XI.014"},{"key":"10130_CR41","doi-asserted-by":"crossref","unstructured":"Ma, P., Du, T., Zhang, J. Z., et al. (2021). DiffAqua: A differentiable computational design pipeline for soft underwater swimmers with shape interpolation. arXiv preprint arXiv:2104.00837.","DOI":"10.1145\/3476576.3476704"},{"issue":"13","key":"10130_CR42","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1177\/0278364913498432","volume":"32","author":"C Majidi","year":"2013","unstructured":"Majidi, C., Shepherd, R. F., Kramer, R. K., et al. (2013). Influence of surface traction on soft robot undulation. The International Journal of Robotics Research, 32(13), 1577\u20131584.","journal-title":"The International Journal of Robotics Research"},{"key":"10130_CR43","doi-asserted-by":"crossref","unstructured":"Morimoto, R., Nishikawa, S., Niiyama, R., et al. (2021). Model-free reinforcement learning with ensemble for a soft continuum robot arm. In Proceedings of the IEEE International Conference on Soft Robotics (RoboSoft).","DOI":"10.1109\/RoboSoft51838.2021.9479340"},{"key":"10130_CR44","doi-asserted-by":"crossref","unstructured":"Morzadec, T., Marcha, D., & Duriez, C. (2019). Toward shape optimization of soft robots. In Proceedings of the IEEE International Conference on Soft Robotics (RoboSoft).","DOI":"10.1109\/ROBOSOFT.2019.8722822"},{"issue":"15","key":"10130_CR45","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1002\/adfm.201303288","volume":"24","author":"B Mosadegh","year":"2014","unstructured":"Mosadegh, B., Polygerinos, P., Keplinger, C., et al. (2014). Pneumatic networks for soft robotics that actuate rapidly. Advanced Functional Materials, 24(15), 2163\u20132170.","journal-title":"Advanced Functional Materials"},{"issue":"1","key":"10130_CR46","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1109\/MRA.2007.339607","volume":"14","author":"S Murata","year":"2007","unstructured":"Murata, S., & Kurokawa, H. (2007). Self-reconfigurable robots. IEEE Robotics & Automation Magazine, 14(1), 71\u201378.","journal-title":"IEEE Robotics & Automation Magazine"},{"issue":"3","key":"10130_CR47","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1115\/1.2899229","volume":"116","author":"JH Park","year":"1994","unstructured":"Park, J. H., & Asada, H. (1994). Concurrent design optimization of mechanical structure and control for high speed robots. Journal of Dynamic Systems, Measurement, and Control, 116(3), 344\u2013356.","journal-title":"Journal of Dynamic Systems, Measurement, and Control"},{"key":"10130_CR48","unstructured":"Pathak, D., Lu, C., Darrell, T., et al. (2019). Learning to control self-assembling morphologies: A study of generalization via modularity. arXiv:1902.05546."},{"key":"10130_CR49","doi-asserted-by":"crossref","unstructured":"Paul, C., & Bongard, J. C. (2001). The road less travelled: Morphology in the optimization of biped robot locomotion. In Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS).","DOI":"10.1109\/IROS.2001.973363"},{"key":"10130_CR50","doi-asserted-by":"crossref","unstructured":"Paul, C., Valero-Cuevas, F. J., & Lipson, H. (2006). Design and control of tensegrity robots for locomotion. Transactions on Robotics 22(5).","DOI":"10.1109\/TRO.2006.878980"},{"key":"10130_CR51","doi-asserted-by":"crossref","unstructured":"Pinskier, J., & Howard, D. (2021). From bioinspiration to computer generation: Developments in autonomous soft robot design. Advanced Intelligent Systems.","DOI":"10.1002\/aisy.202100086"},{"issue":"2","key":"10130_CR52","doi-asserted-by":"publisher","first-page":"419","DOI":"10.5254\/1.3547752","volume":"76","author":"H Qi","year":"2003","unstructured":"Qi, H., Joyce, K., & Boyce, M. (2003). Durometer hardness and the stress-strain behavior of elastomeric materials. Rubber Chemistry and Technology, 76(2), 419\u2013435.","journal-title":"Rubber Chemistry and Technology"},{"key":"10130_CR53","doi-asserted-by":"crossref","unstructured":"Rafsanjani, A., Zhang, Y., Liu, B., et al. (2018). Kirigami skins make a simple soft actuator crawl. Science Robotics 3(15).","DOI":"10.1126\/scirobotics.aar7555"},{"key":"10130_CR54","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1038\/nature14543","volume":"521","author":"D Rus","year":"2015","unstructured":"Rus, D., & Tolley, M. T. (2015). Design, fabrication and control of soft robots. Nature, 521, 467\u2013475.","journal-title":"Nature"},{"key":"10130_CR55","doi-asserted-by":"crossref","unstructured":"Schaff, C., Yunis, D., Chakrabarti, A., et al. (2019). Jointly learning to construct and control agents using deep reinforcement learning. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).","DOI":"10.1109\/ICRA.2019.8793537"},{"key":"10130_CR56","doi-asserted-by":"crossref","unstructured":"Schaff, C., Sedal, A., & Walter, M. R. (2022). Soft robots learn to crawl: Jointly optimizing design and control with sim-to-real transfer. In Proceedings of Robotics: Science and Systems (RSS).","DOI":"10.15607\/RSS.2022.XVIII.062"},{"key":"10130_CR57","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1146\/annurev-control-053018-023834","volume":"2","author":"J Seo","year":"2019","unstructured":"Seo, J., Paik, J., & Yim, M. (2019). Modular reconfigurable robotics. Annual Review of Control, Robotics, and Autonomous Systems, 2, 63\u201388.","journal-title":"Annual Review of Control, Robotics, and Autonomous Systems"},{"issue":"51","key":"10130_CR58","doi-asserted-by":"publisher","first-page":"20,400","DOI":"10.1073\/pnas.1116564108","volume":"108","author":"RF Shepherd","year":"2011","unstructured":"Shepherd, R. F., Ilievski, F., Choi, W., et al. (2011). Multigait soft robot. Proceedings of the National Academy of Sciences, 108(51), 20,400-20,403.","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"10130_CR59","doi-asserted-by":"crossref","unstructured":"Sims, K. (1994). Evolving virtual creatures. In Proceeding of the International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH).","DOI":"10.1145\/192161.192167"},{"key":"10130_CR60","doi-asserted-by":"crossref","unstructured":"Spielberg, A., Araki, B., Sung, C., et al. (2017). Functional co-optimization of articulated robots. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).","DOI":"10.1109\/ICRA.2017.7989587"},{"issue":"2","key":"10130_CR61","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1109\/LRA.2021.3056369","volume":"6","author":"A Spielberg","year":"2021","unstructured":"Spielberg, A., Amini, A., Chin, L., et al. (2021). Co-learning of task and sensor placement for soft robotics. IEEE Robotics and Automation Letters, 6(2), 1208\u20131215.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"10130_CR62","doi-asserted-by":"crossref","unstructured":"Talamini, J., Medvet, E., Bartoli, A., et al. (2019). Evolutionary synthesis of sensing controllers for voxel-based soft robots. In Proceedings of the Artificial Life Conference (ALIFE).","DOI":"10.1162\/isal_a_00223.xml"},{"key":"10130_CR63","doi-asserted-by":"crossref","unstructured":"Tan, J., Zhang, T., Coumans, E., et al. (2018). Sim-to-real: Learning agile locomotion for quadruped robots. In Proceedings of Robotics: Science and Systems (RSS).","DOI":"10.15607\/RSS.2018.XIV.010"},{"issue":"2","key":"10130_CR64","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/s10514-018-9773-y","volume":"43","author":"O Taylor","year":"2019","unstructured":"Taylor, O., & Rodriguez, A. (2019). Optimal shape and motion planning for dynamic planar manipulation. Autonomous Robots, 43(2), 327\u2013344.","journal-title":"Autonomous Robots"},{"key":"10130_CR65","unstructured":"Tiboni, G., Protopapa, A., Tommasi, T., et al. (2023). Domain randomization for robust, affordable and effective closed-loop control of soft robots. arXiv preprint arXiv:2303.04136."},{"key":"10130_CR66","doi-asserted-by":"crossref","unstructured":"Tobin, J., Fong, R., Ray, A., et al. (2017). Domain randomization for transferring deep neural networks from simulation to the real world. In 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp 23\u201330","DOI":"10.1109\/IROS.2017.8202133"},{"key":"10130_CR67","unstructured":"Varghese, N. V., & Mahmoud, Q. (2020). A survey of multi-task deep reinforcement learning. Electronics."},{"issue":"4","key":"10130_CR68","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1109\/TRO.2016.2588888","volume":"32","author":"V Vikas","year":"2016","unstructured":"Vikas, V., Cohen, E., Grassi, R., et al. (2016). Design and locomotion control of a soft robot using friction manipulation and motor-tendon actuation. IEEE Transactions on Robotics, 32(4), 949\u2013959.","journal-title":"IEEE Transactions on Robotics"},{"issue":"5","key":"10130_CR69","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.1109\/TMECH.2012.2208196","volume":"18","author":"MG Villarreal-Cervantes","year":"2013","unstructured":"Villarreal-Cervantes, M. G., Cruz-Villar, C. A., Alvarez-Gallegos, J., et al. (2013). Robust structure-control design approach for mechatronic systems. Transactions on Mechatronics, 18(5), 1592\u20131601.","journal-title":"Transactions on Mechatronics"},{"key":"10130_CR70","doi-asserted-by":"crossref","unstructured":"Vitanov, I., Rizqi, A., & Althoefer, K. (2020). Shape reconstruction of soft-body manipulator: A learning-based approach. In Proceedings of the Annual Conference Towards Autonomous Robotic Systems (TAROS)","DOI":"10.1007\/978-3-030-63486-5_7"},{"issue":"5","key":"10130_CR71","doi-asserted-by":"publisher","first-page":"2100,165","DOI":"10.1002\/aisy.202100165","volume":"4","author":"J Wang","year":"2022","unstructured":"Wang, J., & Chortos, A. (2022). Control strategies for soft robot systems. Advanced Intelligent Systems, 4(5), 2100,165.","journal-title":"Advanced Intelligent Systems"},{"key":"10130_CR72","doi-asserted-by":"crossref","unstructured":"Weiss, J. (2019). A tutorial on the proper orthogonal decomposition. In AIAA Aviation 2019 Forum, p 3333.","DOI":"10.2514\/6.2019-3333"},{"key":"10130_CR73","unstructured":"Whitman, J., Travers, M., & Choset, H. (2021). Learning modular robot control policies. arXiv:2105.10049."},{"key":"10130_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, J. Z., Zhang, Y., Ma, P., et al. (2022). Sim2real for soft robotic fish via differentiable simulation. In 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp 12,598\u201312,605.","DOI":"10.1109\/IROS47612.2022.9981338"},{"key":"10130_CR75","doi-asserted-by":"crossref","unstructured":"Zhao, A., Xu, J., Konakovi\u0107-Lukovi\u0107, M., et al. (2020). Robogrammar: Graph grammar for terrain-optimized robot design. ACM Transactions on Graphics (TOG) 39(6).","DOI":"10.1145\/3414685.3417831"},{"key":"10130_CR76","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Rossiter, J., & Hauser, H. (2019). Learning in growing robots: Knowledge transfer from tadpole to frog robot. In Proceedings of the International Conference on Biomimetic and Biohybrid Systems.","DOI":"10.1007\/978-3-030-24741-6_42"}],"container-title":["Autonomous Robots"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10514-023-10130-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10514-023-10130-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10514-023-10130-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T18:11:28Z","timestamp":1701195088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10514-023-10130-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,8]]},"references-count":76,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10130"],"URL":"https:\/\/doi.org\/10.1007\/s10514-023-10130-8","relation":{},"ISSN":["0929-5593","1573-7527"],"issn-type":[{"value":"0929-5593","type":"print"},{"value":"1573-7527","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,8]]},"assertion":[{"value":"1 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Code is available at the framework\u2019s Github Page: . Data is available at .","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and materials"}}]}}