{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:04:34Z","timestamp":1777593874709,"version":"3.51.4"},"reference-count":179,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T00:00:00Z","timestamp":1547769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Singapore Academic Research Fund","award":["R-397-000-227-112"],"award-info":[{"award-number":["R-397-000-227-112"]}]},{"name":"NUSRI China Jiangsu Provincial Grants","award":["BK20150386"],"award-info":[{"award-number":["BK20150386"]}]},{"name":"NUSRI China Jiangsu Provincial Grants","award":["BE2016077"],"award-info":[{"award-number":["BE2016077"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Robotics"],"abstract":"<jats:p>The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics.<\/jats:p>","DOI":"10.3390\/robotics8010004","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T11:26:55Z","timestamp":1547810815000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":111,"title":["Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9067-0926","authenticated-orcid":false,"given":"Sarthak","family":"Bhagat","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering, 4 Engineering Drive 3, National University of Singapore, Singapore 117583, Singapore"},{"name":"Department of Electronics and Communications Engineering, Indraprastha Institute of Information Technology, New Delhi 110020, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2939-646X","authenticated-orcid":false,"given":"Hritwick","family":"Banerjee","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering, 4 Engineering Drive 3, National University of Singapore, Singapore 117583, Singapore"},{"name":"Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore 117456, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9741-1137","authenticated-orcid":false,"given":"Zion Tsz","family":"Ho Tse","sequence":"additional","affiliation":[{"name":"School of Electrical &amp; Computer Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6488-1551","authenticated-orcid":false,"given":"Hongliang","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering, 4 Engineering Drive 3, National University of Singapore, Singapore 117583, Singapore"},{"name":"Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore 117456, Singapore"},{"name":"National University of Singapore (Suzhou) Research Institute (NUSRI), Suzhou Industrial Park, Suzhou 215123, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1089\/soro.2014.1503","article-title":"A Confluence of Technology: Putting Biology into Robotics","volume":"1","author":"Trimmer","year":"2014","journal-title":"Soft Robot."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Banerjee, H., Tse, Z.T.H., and Ren, H. (2018). Soft Robotics with Compliance and Adaptation for Biomedical Applications and Forthcoming Challenges. Int. J. Robot. Autom., 33.","DOI":"10.2316\/Journal.206.2018.1.206-4981"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1080\/11762320802557865","article-title":"Soft robotics: Biological inspiration, state of the art, and future research","volume":"5","author":"Trivedi","year":"2008","journal-title":"Appl. Bionics Biomech."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Banerjee, H., and Ren, H. (2018). Electromagnetically responsive soft-flexible robots and sensors for biomedical applications and impending challenges. Electromagnetic Actuation and Sensing in Medical Robotics, Springer.","DOI":"10.1007\/978-981-10-6035-9_3"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Banerjee, H., Aaron, O.Y.W., Yeow, B.S., and Ren, H. (2018, January 18\u201320). Fabrication and Initial Cadaveric Trials of Bi-directional Soft Hydrogel Robotic Benders Aiming for Biocompatible Robot-Tissue Interactions. Proceedings of the IEEE ICARM 2018, Singapore.","DOI":"10.1109\/ICARM.2018.8610717"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Banerjee, H., Roy, B., Chaudhury, K., Srinivasan, B., Chakraborty, S., and Ren, H. (2018). Frequency-induced morphology alterations in microconfined biological cells. Med. Biol. Eng. Comput.","DOI":"10.1007\/s11517-018-1908-y"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.tibtech.2013.03.002","article-title":"Soft robotics: A bioinspired evolution in robotics","volume":"31","author":"Kim","year":"2013","journal-title":"Trends Biotechnol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ren, H., and Banerjee, H. (2018). A Preface in Electromagnetic Robotic Actuation and Sensing in Medicine. Electromagnetic Actuation and Sensing in Medical Robotics, Springer.","DOI":"10.1007\/978-981-10-6035-9"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Banerjee, H., Shen, S., and Ren, H. (2018). Magnetically Actuated Minimally Invasive Microbots for Biomedical Applications. Electromagnetic Actuation and Sensing in Medical Robotics, Springer.","DOI":"10.1007\/978-981-10-6035-9_2"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Banerjee, H., Suhail, M., and Ren, H. (2018). Hydrogel Actuators and Sensors for Biomedical Soft Robots: Brief Overview with Impending Challenges. Biomimetics, 3.","DOI":"10.3390\/biomimetics3030015"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.procs.2011.12.030","article-title":"Soft robotics: Challenges and perspectives","volume":"7","author":"Iida","year":"2011","journal-title":"Proc. Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_13","unstructured":"Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Abbeel, O.P., and Zaremba, W. (2017, January 4\u20139). Hindsight experience replay. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Deng, L. (2014). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Signal Inf. Process., 3.","DOI":"10.1017\/atsip.2013.9"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_16","unstructured":"Bagnell, J.A. (2015). An Invitation to Imitation, Carnegie-Mellon Univ Pittsburgh Pa Robotics Inst. Technical Report."},{"key":"ref_17","unstructured":"Levine, S. (2013). Exploring Deep and Recurrent Architectures for Optimal Control. arXiv."},{"key":"ref_18","unstructured":"Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015). Continuous Control with Deep Reinforcement Learning. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Spielberg, S., Gopaluni, R.B., and Loewen, P.D. (2017, January 28\u201331). Deep reinforcement learning approaches for process control. Proceedings of the 2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP), Taipei, Taiwan.","DOI":"10.1109\/ADCONIP.2017.7983780"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.sna.2017.07.001","article-title":"Large area and flexible micro-porous piezoelectric materials for soft robotic skin","volume":"263","author":"Khanbareh","year":"2017","journal-title":"Sens. Actuators A Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"eaai7529","DOI":"10.1126\/scirobotics.aai7529","article-title":"Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides","volume":"1","author":"Zhao","year":"2016","journal-title":"Sci. Robot."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"13132","DOI":"10.1073\/pnas.1713450114","article-title":"Fluid-driven origami-inspired artificial muscles","volume":"114","author":"Li","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3054","DOI":"10.1177\/1045389X17704921","article-title":"Experimental characterization of a dielectric elastomer fluid pump and optimizing performance via composite materials","volume":"28","author":"Ho","year":"2017","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"20400","DOI":"10.1073\/pnas.1116564108","article-title":"Multigait soft robot","volume":"108","author":"Shepherd","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"064501","DOI":"10.1115\/1.4041200","article-title":"Single-Motor Controlled Tendon-Driven Peristaltic Soft Origami Robot","volume":"10","author":"Banerjee","year":"2018","journal-title":"J. Mech. Robot."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sutton, R.S., and Barto, A.G. (1998). Reinforcement Learning: An Introduction, MIT Press.","DOI":"10.1109\/TNN.1998.712192"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1162\/neco.1993.5.4.613","article-title":"Improving generalization for temporal difference learning: The successor representation","volume":"5","author":"Dayan","year":"1993","journal-title":"Neural Comput."},{"key":"ref_28","unstructured":"Kulkarni, T.D., Saeedi, A., Gautam, S., and Gershman, S.J. (2016). Deep Successor Reinforcement Learning. arXiv."},{"key":"ref_29","unstructured":"Barreto, A., Dabney, W., Munos, R., Hunt, J.J., Schaul, T., van Hasselt, H.P., and Silver, D. (2017, January 4\u20139). Successor features for transfer in reinforcement learning. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, J., Springenberg, J.T., Boedecker, J., and Burgard, W. (2017, January 24\u201328). Deep reinforcement learning with successor features for navigation across similar environments. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206049"},{"key":"ref_31","unstructured":"Fu, M.C., Glover, F.W., and April, J. (2005, January 4\u20137). Simulation optimization: A review, new developments, and applications. Proceedings of the 37th Conference on Winter Simulation, Orlando, FL, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2936","DOI":"10.1162\/neco.2006.18.12.2936","article-title":"Learning Tetris using the noisy cross-entropy method","volume":"18","author":"Szita","year":"2006","journal-title":"Neural Comput."},{"key":"ref_33","unstructured":"Schulman, J., Moritz, P., Levine, S., Jordan, M., and Abbeel, P. (2015). High-Dimensional Continuous Control Using Generalized Advantage Estimation. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1023\/A:1022672621406","article-title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning","volume":"8","author":"Williams","year":"1992","journal-title":"Mach. Learn."},{"key":"ref_35","unstructured":"Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., and Riedmiller, M. (2014, January 21\u201326). Deterministic policy gradient algorithms. Proceedings of the ICML, Beijing, China."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1145\/122344.122377","article-title":"Dyna, an integrated architecture for learning, planning, and reacting","volume":"2","author":"Sutton","year":"1991","journal-title":"ACM SIGART Bull."},{"key":"ref_37","unstructured":"Weber, T., Racani\u00e8re, S., Reichert, D.P., Buesing, L., Guez, A., Rezende, D.J., Badia, A.P., Vinyals, O., Heess, N., and Li, Y. (2017). Imagination-Augmented Agents for Deep Reinforcement Learning. arXiv."},{"key":"ref_38","unstructured":"Kalweit, G., and Boedecker, J. (2017, January 13\u201315). Uncertainty-driven imagination for continuous deep reinforcement learning. Proceedings of the Conference on Robot Learning, Mountain View, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Banerjee, H., Pusalkar, N., and Ren, H. (2018, January 12\u201315). Preliminary Design and Performance Test of Tendon-Driven Origami-Inspired Soft Peristaltic Robot. Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2018), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ROBIO.2018.8664842"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1089\/soro.2014.0001","article-title":"Soft Robotics Technologies to Address Shortcomings in Today\u2019s Minimally Invasive Surgery: The STIFF-FLOP Approach","volume":"1","author":"Cianchetti","year":"2014","journal-title":"Soft Robot."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"eaan3028","DOI":"10.1126\/scirobotics.aan3028","article-title":"A soft robot that navigates its environment through growth","volume":"2","author":"Hawkes","year":"2017","journal-title":"Sci. Robot."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Atalay, O., Atalay, A., Gafford, J., and Walsh, C. (2017). A Highly Sensitive Capacitive-Based Soft Pressure Sensor Based on a Conductive Fabric and a Microporous Dielectric Layer. Adv. Mater.","DOI":"10.1002\/admt.201700237"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e1706383","DOI":"10.1002\/adma.201706383","article-title":"Soft Somatosensitive Actuators via Embedded 3D Printing","volume":"30","author":"Truby","year":"2018","journal-title":"Adv. Mater."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1109\/TRO.2015.2409452","article-title":"Design and Modeling of Generalized Fiber-Reinforced Pneumatic Soft Actuators","volume":"31","author":"Kota","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"eaar3449","DOI":"10.1126\/scirobotics.aar3449","article-title":"Exploration of underwater life with an acoustically controlled soft robotic fish","volume":"3","author":"Katzschmann","year":"2018","journal-title":"Sci. Robot."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Van Hasselt, H., Guez, A., and Silver, D. (2016, January 12\u201317). Deep Reinforcement Learning with Double Q-Learning. Proceedings of the AAAI, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"ref_48","unstructured":"Wang, Z., Schaul, T., Hessel, M., Van Hasselt, H., Lanctot, M., and De Freitas, N. (2015). Dueling Network Architectures for Deep Reinforcement Learning. arXiv."},{"key":"ref_49","unstructured":"Gu, S., Lillicrap, T., Sutskever, I., and Levine, S. (2016, January 19\u201324). Continuous deep q-learning with model-based acceleration. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gu, S., Holly, E., Lillicrap, T., and Levine, S. (June, January 29). Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989385"},{"key":"ref_51","unstructured":"Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016, January 19\u201324). Asynchronous methods for deep reinforcement learning. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_52","unstructured":"Wang, J.X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J.Z., Munos, R., Blundell, C., Kumaran, D., and Botvinick, M. (2016). Learning to Reinforcement Learn. arXiv."},{"key":"ref_53","unstructured":"Wu, Y., Mansimov, E., Grosse, R.B., Liao, S., and Ba, J. (2017, January 4\u20139). Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_54","unstructured":"Levine, S., and Koltun, V. (June, January 16). Guided policy search. Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_55","unstructured":"Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P. (July, January 6). Trust region policy optimization. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_56","unstructured":"Kakade, S., and Langford, J. (2002, January 8\u201312). Approximately optimal approximate reinforcement learning. Proceedings of the ICML, Sydney, Australia."},{"key":"ref_57","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017). Proximal Policy Optimization Algorithms. arXiv."},{"key":"ref_58","unstructured":"Mirowski, P., Pascanu, R., Viola, F., Soyer, H., Ballard, A.J., Banino, A., Denil, M., Goroshin, R., Sifre, L., and Kavukcuoglu, K. (2016). Learning to Navigate in Complex Environments. arXiv."},{"key":"ref_59","unstructured":"Riedmiller, M., Hafner, R., Lampe, T., Neunert, M., Degrave, J., Van de Wiele, T., Mnih, V., Heess, N., and Springenberg, J.T. (2018). Learning by Playing-Solving Sparse Reward Tasks from Scratch. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yu, T., Finn, C., Xie, A., Dasari, S., Zhang, T., Abbeel, P., and Levine, S. (2018). One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. arXiv.","DOI":"10.15607\/RSS.2018.XIV.002"},{"key":"ref_61","first-page":"1334","article-title":"End-to-end training of deep visuomotor policies","volume":"17","author":"Levine","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_62","unstructured":"Jaderberg, M., Mnih, V., Czarnecki, W.M., Schaul, T., Leibo, J.Z., Silver, D., and Kavukcuoglu, K. (2016). Reinforcement Learning with Unsupervised Auxiliary Tasks. arXiv."},{"key":"ref_63","unstructured":"Schaul, T., Quan, J., Antonoglou, I., and Silver, D. (2015). Prioritized Experience Replay. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., and Weston, J. (2009, January 14\u201318). Curriculum learning. Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553380"},{"key":"ref_65","unstructured":"Zhang, J., Tai, L., Boedecker, J., Burgard, W., and Liu, M. (2017). Neural SLAM. arXiv."},{"key":"ref_66","unstructured":"Florensa, C., Held, D., Wulfmeier, M., Zhang, M., and Abbeel, P. (2017). Reverse Curriculum Generation for Reinforcement Learning. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Pathak, D., Agrawal, P., Efros, A.A., and Darrell, T. (2017, January 6\u201311). Curiosity-driven exploration by self-supervised prediction. Proceedings of the International Conference on Machine Learning (ICML), Sydney, Australia.","DOI":"10.1109\/CVPRW.2017.70"},{"key":"ref_68","unstructured":"Sukhbaatar, S., Lin, Z., Kostrikov, I., Synnaeve, G., Szlam, A., and Fergus, R. (2017). Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play. arXiv."},{"key":"ref_69","unstructured":"Fortunato, M., Azar, M.G., Piot, B., Menick, J., Osband, I., Graves, A., Mnih, V., Munos, R., Hassabis, D., and Pietquin, O. (2017). Noisy Networks for Exploration. arXiv."},{"key":"ref_70","unstructured":"Plappert, M., Houthooft, R., Dhariwal, P., Sidor, S., Chen, R.Y., Chen, X., Asfour, T., Abbeel, P., and Andrychowicz, M. (2017). Parameter Space Noise for Exploration. arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Rafsanjani, A., Zhang, Y., Liu, B., Rubinstein, S.M., and Bertoldi, K. Kirigami skins make a simple soft actuator crawl. Sci. Robot., 2018.","DOI":"10.1126\/scirobotics.aar7555"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Mottaghi, R., Kolve, E., Lim, J.J., Gupta, A., Fei-Fei, L., and Farhadi, A. (2017, January 30\u201331). Target-driven visual navigation in indoor scenes using deep reinforcement learning. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989381"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_74","unstructured":"Kolve, E., Mottaghi, R., Gordon, D., Zhu, Y., Gupta, A., and Farhadi, A. (2017). AI2-THOR: An Interactive 3d Environment for Visual AI. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Tai, L., Paolo, G., and Liu, M. (2017, January 24\u201328). Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202134"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Chen, Y.F., Everett, M., Liu, M., and How, J.P. (2017, January 24\u201328). Socially aware motion planning with deep reinforcement learning. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202312"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Long, P., Fan, T., Liao, X., Liu, W., Zhang, H., and Pan, J. (2017). Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning. arXiv.","DOI":"10.1109\/ICRA.2018.8461113"},{"key":"ref_78","unstructured":"Thrun, S., Burgard, W., and Fox, D. (2001). Probabilistic Robotics (Intelligent Robotics and Autonomous Agents), The MIT Press."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Gupta, S., Davidson, J., Levine, S., Sukthankar, R., and Malik, J. (2017). Cognitive Mapping and Planning for Visual Navigation. arXiv, 3.","DOI":"10.1109\/CVPR.2017.769"},{"key":"ref_80","unstructured":"Gupta, S., Fouhey, D., Levine, S., and Malik, J. (2017). Unifying Map and Landmark Based Representations for Visual Navigation. arXiv."},{"key":"ref_81","unstructured":"Parisotto, E., and Salakhutdinov, R. (2017). Neural Map: Structured Memory for Deep Reinforcement Learning. arXiv."},{"key":"ref_82","unstructured":"K\u00fcmmerle, R., Grisetti, G., Strasdat, H., Konolige, K., and Burgard, W. (2011, January 9\u201313). G2o: A general framework for graph optimization. Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Parisotto, E., Chaplot, D.S., Zhang, J., and Salakhutdinov, R. (2018). Global Pose Estimation with an Attention-Based Recurrent Network. arXiv.","DOI":"10.1109\/CVPRW.2018.00061"},{"key":"ref_84","unstructured":"Schaul, T., Horgan, D., Gregor, K., and Silver, D. (July, January 6). Universal value function approximators. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. arXiv.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_86","unstructured":"Khan, A., Zhang, C., Atanasov, N., Karydis, K., Kumar, V., and Lee, D.D. (2017). Memory Augmented Control Networks. arXiv."},{"key":"ref_87","unstructured":"Bruce, J., S\u00fcnderhauf, N., Mirowski, P., Hadsell, R., and Milford, M. (2017). One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay. arXiv."},{"key":"ref_88","unstructured":"Chaplot, D.S., Parisotto, E., and Salakhutdinov, R. (2018). Active Neural Localization. arXiv."},{"key":"ref_89","unstructured":"Savinov, N., Dosovitskiy, A., and Koltun, V. (2018). Semi-Parametric Topological Memory for Navigation. arXiv."},{"key":"ref_90","unstructured":"Heess, N., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, A., and Riedmiller, M. (2017). Emergence of Locomotion Behaviours in Rich Environments. arXiv."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"036002","DOI":"10.1088\/1748-3182\/6\/3\/036002","article-title":"An octopus-bioinspired solution to movement and manipulation for soft robots","volume":"6","author":"Calisti","year":"2011","journal-title":"Bioinspir. Biomim."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1002\/adma.201203002","article-title":"Robotic tentacles with three-dimensional mobility based on flexible elastomers","volume":"25","author":"Martinez","year":"2013","journal-title":"Adv. Mater."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Caldera, S. (2018). Review of Deep Learning Methods in Robotic Grasp Detection. Multimodal Technol. Interact., 2.","DOI":"10.20944\/preprints201805.0484.v1"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1109\/LRA.2017.2716445","article-title":"A Soft-Robotic Gripper with Enhanced Object Adaptation and Grasping Reliability","volume":"2","author":"Zhou","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Finn, C., Tan, X.Y., Duan, Y., Darrell, T., Levine, S., and Abbeel, P. (2015). Deep Spatial Autoencoders for Visuomotor Learning. arXiv.","DOI":"10.1109\/ICRA.2016.7487173"},{"key":"ref_96","unstructured":"Tzeng, E., Devin, C., Hoffman, J., Finn, C., Peng, X., Levine, S., Saenko, K., and Darrell, T. (2015). Towards Adapting Deep Visuomotor Representations from Simulated to Real Environments. arXiv."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Fu, J., Levine, S., and Abbeel, P. (2016, January 9\u201314). One-shot learning of manipulation skills with online dynamics adaptation and neural network priors. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759592"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Kumar, V., Todorov, E., and Levine, S. (2016, January 16\u201320). Optimal control with learned local models: Application to dexterous manipulation. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), New York, NY, USA.","DOI":"10.1109\/ICRA.2016.7487156"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Gupta, A., Eppner, C., Levine, S., and Abbeel, P. (2016, January 9\u201314). Learning dexterous manipulation for a soft robotic hand from human demonstrations. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Deajeon, Korea.","DOI":"10.1109\/IROS.2016.7759557"},{"key":"ref_100","unstructured":"Popov, I., Heess, N., Lillicrap, T., Hafner, R., Barth-Maron, G., Vecerik, M., Lampe, T., Tassa, Y., Erez, T., and Riedmiller, M. (2017). Data-Efficient Deep Reinforcement Learning for Dexterous manipulation. arXiv."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"3580","DOI":"10.1109\/JSEN.2018.2817211","article-title":"Electromagnetically Enhanced Soft and Flexible Bend Sensor: A Quantitative Analysis with Different Cores","volume":"18","author":"Prituja","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/nature11409","article-title":"Highly stretchable and tough hydrogels","volume":"489","author":"Sun","year":"2012","journal-title":"Nature"},{"key":"ref_103","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., and Darrell, T. (2014). Deep Domain Confusion: Maximizing for Domain Invariance. arXiv."},{"key":"ref_104","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems (NIPS 2014), Montreal, QC, Canada."},{"key":"ref_105","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv."},{"key":"ref_106","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein gan. arXiv."},{"key":"ref_107","unstructured":"Hoffman, J., Tzeng, E., Park, T., Zhu, J.Y., Isola, P., Saenko, K., Efros, A.A., and Darrell, T. (2017). Cycada: Cycle-Consistent Adversarial Domain Adaptation. arXiv."},{"key":"ref_108","unstructured":"Doersch, C. (2016). Tutorial on Variational Autoencoders. arXiv."},{"key":"ref_109","unstructured":"Szab\u00f3, A., Hu, Q., Portenier, T., Zwicker, M., and Favaro, P. (2017). Challenges in Disentangling Independent Factors of Variation. arXiv."},{"key":"ref_110","unstructured":"Mathieu, M., Zhao, J.J., Sprechmann, P., Ramesh, A., and LeCun, Y. (2016, January 5\u201310). Disentangling factors of variation in deep representations using adversarial training. Proceedings of the NIPS 2016, Barcelona, Spain."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., Kalakrishnan, M., Downs, L., Ibarz, J., Pastor, P., and Konolige, K. (2017). Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping. arXiv.","DOI":"10.1109\/ICRA.2018.8460875"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P. (2017, January 24\u201328). Domain randomization for transferring deep neural networks from simulation to the real world. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202133"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Peng, X.B., Andrychowicz, M., Zaremba, W., and Abbeel, P. (2017). Sim-to-Real Transfer of Robotic Control with Dynamics Randomization. arXiv.","DOI":"10.1109\/ICRA.2018.8460528"},{"key":"ref_114","unstructured":"Rusu, A.A., Vecerik, M., Roth\u00f6rl, T., Heess, N., Pascanu, R., and Hadsell, R. (2016). Sim-to-Real Robot Learning from Pixels with Progressive Nets. arXiv."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Zhang, J., Tai, L., Xiong, Y., Liu, M., Boedecker, J., and Burgard, W. (2018). Vr Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control. arXiv.","DOI":"10.1109\/LRA.2019.2894216"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1007\/s11263-018-1089-z","article-title":"Artistic style transfer for videos and spherical images","volume":"126","author":"Ruder","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_117","first-page":"2149","article-title":"Design and use paradigms for Gazebo, an open-source multi-robot simulator","volume":"4","author":"Koenig","year":"2004","journal-title":"IROS. Citeseer"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0278364916679498","article-title":"1 year, 1000 km: The Oxford RobotCar dataset","volume":"36","author":"Maddern","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_119","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017). CARLA: An Open Urban Driving Simulator. arXiv."},{"key":"ref_120","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected Crfs. arXiv."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Yang, L., Liang, X., and Xing, E. (2018). Unsupervised Real-to-Virtual Domain Unification for End-to-End Highway Driving. arXiv.","DOI":"10.1007\/978-3-030-01225-0_33"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1089\/soro.2015.0014","article-title":"Contractile performance and controllability of insect muscle-powered bioactuator with different stimulation strategies for soft robotics","volume":"3","author":"Uesugi","year":"2016","journal-title":"Soft Robot."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1089\/soro.2014.0023","article-title":"Pouch Motors: Printable Soft Actuators Integrated with Computational Design","volume":"2","author":"Niiyama","year":"2015","journal-title":"Soft Robot."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/14686996.2018.1431862","article-title":"3D printing for soft robotics\u2014A review","volume":"19","author":"Gul","year":"2018","journal-title":"Sci. Technol. Adv. Mater."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Umedachi, T., Vikas, V., and Trimmer, B. (2013, January 3\u20137). Highly deformable 3-D printed soft robot generating inching and crawling locomotions with variable friction legs. Proceedings of the 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6697016"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Mutlu, R., Tawk, C., Alici, G., and Sariyildiz, E. (November, January 29). A 3D printed monolithic soft gripper with adjustable stiffness. Proceedings of the IECON 2017\u201443rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, China.","DOI":"10.1109\/IECON.2017.8217084"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1089\/soro.2013.0005","article-title":"Flexible and Stretchable Electronics Paving the Way for Soft Robotics","volume":"1","author":"Lu","year":"2014","journal-title":"Soft Robot."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Rohmer, E., Singh, S.P., and Freese, M. (2013, January 3\u20138). V-REP: A versatile and scalable robot simulation framework. Proceedings of the 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696520"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Shah, S., Dey, D., Lovett, C., and Kapoor, A. (2018). Airsim: High-fidelity visual and physical simulation for autonomous vehicles. Field and Service Robotics, Springer.","DOI":"10.1007\/978-3-319-67361-5_40"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Pan, X., You, Y., Wang, Z., and Lu, C. (2017). Virtual to Real Reinforcement Learning for Autonomous Driving. arXiv.","DOI":"10.5244\/C.31.11"},{"key":"ref_131","unstructured":"Savva, M., Chang, A.X., Dosovitskiy, A., Funkhouser, T., and Koltun, V. (2017). MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments. arXiv."},{"key":"ref_132","unstructured":"Wu, Y., Wu, Y., Gkioxari, G., and Tian, Y. (2018). Building Generalizable Agents with a Realistic and Rich 3D Environment. arXiv."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1080\/01691864.2017.1395362","article-title":"Software toolkit for modeling, simulation, and control of soft robots","volume":"31","author":"Coevoet","year":"2017","journal-title":"Adv. Robot."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Duriez, C., Coevoet, E., Largilliere, F., Bieze, T.M., Zhang, Z., Sanz-Lopez, M., Carrez, B., Marchal, D., Goury, O., and Dequidt, J. (2016, January 13\u201316). Framework for online simulation of soft robots with optimization-based inverse model. Proceedings of the 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), San Francisco, CA, USA.","DOI":"10.1109\/SIMPAR.2016.7862384"},{"key":"ref_135","first-page":"1460","article-title":"Analysis of 3 RPS Robotic Platform Motion in SimScape and MATLAB GUI Environment","volume":"12","author":"Olaya","year":"2017","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1109\/LRA.2017.2669367","article-title":"Optimization-based inverse model of soft robots with contact handling","volume":"2","author":"Coevoet","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1152\/jn.00684.2004","article-title":"Dynamic model of the octopus arm. I. Biomechanics of the octopus reaching movement","volume":"94","author":"Yekutieli","year":"2005","journal-title":"J. Neurophysiol."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1089\/soro.2017.0019","article-title":"Fully soft 3D-printed electroactive fluidic valve for soft hydraulic robots","volume":"5","author":"Zatopa","year":"2018","journal-title":"Soft Robot."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Ratliff, N.D., Bagnell, J.A., and Srinivasa, S.S. (December, January 29). Imitation learning for locomotion and manipulation. Proceedings of the 2007 7th IEEE-RAS International Conference on Humanoid Robots, Pittsburgh, PA, USA.","DOI":"10.1109\/ICHR.2007.4813899"},{"key":"ref_140","unstructured":"Langsfeld, J.D., Kaipa, K.N., Gentili, R.J., Reggia, J.A., and Gupta, S.K. (2019, January 18). Towards Imitation Learning of Dynamic Manipulation Tasks: A Framework to Learn from Failures. Available online: https:\/\/pdfs.semanticscholar.org\/5e1a\/d502aeb5a800f458390ad1a13478d0fbd39b.pdf."},{"key":"ref_141","unstructured":"Ross, S., Gordon, G., and Bagnell, D. (2011, January 11\u201313). A reduction of imitation learning and structured prediction to no-regret online learning. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_142","unstructured":"Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., and Zhang, J. (2016). End to end Learning for Self-Driving Cars. arXiv."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Tai, L., Li, S., and Liu, M. (2016, January 9\u201314). A deep-network solution towards model-less obstacle avoidance. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759428"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/LRA.2015.2509024","article-title":"A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots","volume":"1","author":"Giusti","year":"2016","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Codevilla, F., M\u00fcller, M., Dosovitskiy, A., L\u00f3pez, A., and Koltun, V. (2017). End-to-End Driving via Conditional Imitation Learning. arXiv.","DOI":"10.1109\/ICRA.2018.8460487"},{"key":"ref_146","unstructured":"Duan, Y., Andrychowicz, M., Stadie, B.C., Ho, J., Schneider, J., Sutskever, I., Abbeel, P., and Zaremba, W. (2017, January 4\u20139). One-Shot Imitation Learning. Proceedings of the NIPS, Long Beach, CA, USA."},{"key":"ref_147","unstructured":"Finn, C., Yu, T., Zhang, T., Abbeel, P., and Levine, S. (2017). One-Shot Visual Imitation Learning via Meta-Learning. arXiv."},{"key":"ref_148","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. arXiv."},{"key":"ref_149","unstructured":"Eitel, A., Hauff, N., and Burgard, W. (2017). Learning to Singulate Objects Using a Push Proposal Network. arXiv."},{"key":"ref_150","unstructured":"Ziebart, B.D., Maas, A.L., Bagnell, J.A., and Dey, A.K. (2008, January 13\u201317). Maximum Entropy Inverse Reinforcement Learning. Proceedings of the AAAI, Chicago, IL, USA."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Okal, B., and Arras, K.O. (2016, January 16\u201320). Learning socially normative robot navigation behaviors with bayesian inverse reinforcement learning. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487452"},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Pfeiffer, M., Schwesinger, U., Sommer, H., Galceran, E., and Siegwart, R. (2016, January 9\u201314). Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Deajeon, Korea.","DOI":"10.1109\/IROS.2016.7759329"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1177\/0278364915619772","article-title":"Socially compliant mobile robot navigation via inverse reinforcement learning","volume":"35","author":"Kretzschmar","year":"2016","journal-title":"Int. J. Robot. Res."},{"key":"ref_154","unstructured":"Wulfmeier, M., Ondruska, P., and Posner, I. (2015). Maximum Entropy Deep Inverse Reinforcement Learning. arXiv."},{"key":"ref_155","unstructured":"Ho, J., and Ermon, S. (2016, January 5\u201310). Generative adversarial imitation learning. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_156","unstructured":"Baram, N., Anschel, O., and Mannor, S. (2016). Model-Based Adversarial Imitation Learning. arXiv."},{"key":"ref_157","unstructured":"Wang, Z., Merel, J.S., Reed, S.E., de Freitas, N., Wayne, G., and Heess, N. (2017, January 4\u20139). Robust imitation of diverse behaviors. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_158","unstructured":"Li, Y., Song, J., and Ermon, S. (2017). Inferring the Latent Structure of Human Decision-Making from Raw Visual Inputs. arXiv."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Tai, L., Zhang, J., Liu, M., and Burgard, W. (2017). Socially-Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning. arXiv.","DOI":"10.1109\/ICRA.2018.8460968"},{"key":"ref_160","unstructured":"Stadie, B.C., Abbeel, P., and Sutskever, I. (2017). Third-Person Imitation Learning. arXiv."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Wehner, M., Truby, R.L., Fitzgerald, D.J., Mosadegh, B., Whitesides, G.M., Lewis, J.A., and Wood, R.J. (2016). An integrated design and fabrication strategy for entirely soft, autonomous robots. Nature, 536.","DOI":"10.1038\/nature19100"},{"key":"ref_162","unstructured":"Katzschmann, R.K., de Maille, A., Dorhout, D.L., and Rus, D. (September, January August). Physical human interaction for an inflatable manipulator. Proceedings of the 2011 IEEE\/EMBC Annual International Conference of the Engineering in Medicine and Biology Society, Boston, MA, USA."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Rog\u00f3z, M., Zeng, H., Xuan, C., Wiersma, D.S., and Wasylczyk, P. (2016). Light-driven soft robot mimics caterpillar locomotion in natural scale. Adv. Opt. Mater., 4.","DOI":"10.1002\/adom.201600503"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Katzschmann, R.K., Marchese, A.D., and Rus, D. (2014, January 15\u201318). Hydraulic Autonomous Soft Robotic Fish for 3D Swimming. Proceedings of the ISER, Marrakech and Essaouira, Morocco.","DOI":"10.1007\/978-3-319-23778-7_27"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Katzschmann, R.K., de Maille, A., Dorhout, D.L., and Rus, D. (2016, January 9\u201314). Cyclic hydraulic actuation for soft robotic devices. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759472"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"DelPreto, J., Katzschmann, R.K., MacCurdy, R.B., and Rus, D. (2015, January 22\u201324). A Compact Acoustic Communication Module for Remote Control Underwater. Proceedings of the WUWNet, Washington, DC, USA.","DOI":"10.1145\/2831296.2831337"},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Marchese, A.D., Onal, C.D., and Rus, D. (2012, January 17\u201321). Towards a Self-contained Soft Robotic Fish: On-Board Pressure Generation and Embedded Electro-permanent Magnet Valves. Proceedings of the ISER, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-00065-7_4"},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/LRA.2017.2779802","article-title":"Transforming the Dynamic Response of Robotic Structures and Systems Through Laminar Jamming","volume":"3","author":"Narang","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"1707136","DOI":"10.1002\/adfm.201707136","article-title":"Mechanically Versatile Soft Machines Through Laminar Jamming","volume":"28","author":"Narang","year":"2017","journal-title":"Adv. Funct. Mater."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"3216","DOI":"10.1109\/LRA.2018.2850971","article-title":"Soft Inflatable Sensing Modules for Safe and Interactive Robots","volume":"3","author":"Kim","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Qi, R., Lam, T.L., and Xu, Y. (June, January 31). Mechanical design and implementation of a soft inflatable robot arm for safe human-robot interaction. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907362"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"1700224","DOI":"10.1002\/marc.201700224","article-title":"Light-Driven, Caterpillar-Inspired Miniature Inching Robot","volume":"39","author":"Zeng","year":"2018","journal-title":"Macromol. Rapid Commun."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1089\/soro.2016.0059","article-title":"Optimizing double-network hydrogel for biomedical soft robots","volume":"4","author":"Banerjee","year":"2017","journal-title":"Soft Robot."},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D. (2017). Deep Reinforcement Learning that Matters. arXiv.","DOI":"10.1609\/aaai.v32i1.11694"},{"key":"ref_175","unstructured":"Vecer\u00edk, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Roth\u00f6rl, T., Lampe, T., and Riedmiller, M.A. (2017). Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards. arXiv."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., and Abbeel, P. (2017). Overcoming Exploration in Reinforcement Learning with Demonstrations. arXiv.","DOI":"10.1109\/ICRA.2018.8463162"},{"key":"ref_177","unstructured":"Gao, Y., Lin, J., Yu, F., Levine, S., and Darrell, T. (2018). Reinforcement Learning from Imperfect Demonstrations. arXiv."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Wang, Z., Merel, J., Rusu, A., Erez, T., Cabi, S., Tunyasuvunakool, S., Kram\u00e1r, J., Hadsell, R., and de Freitas, N. (2018). Reinforcement and Imitation Learning for Diverse Visuomotor Skills. arXiv.","DOI":"10.15607\/RSS.2018.XIV.009"},{"key":"ref_179","unstructured":"Nichol, A., and Schulman, J. (2018). Reptile: A Scalable Metalearning Algorithm. arXiv."}],"updated-by":[{"DOI":"10.3390\/robotics8040093","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T00:00:00Z","timestamp":1547769600000}}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/8\/1\/4\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T23:17:19Z","timestamp":1754263039000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/8\/1\/4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,18]]},"references-count":179,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["robotics8010004"],"URL":"https:\/\/doi.org\/10.3390\/robotics8010004","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints201811.0510.v2","asserted-by":"object"},{"id-type":"doi","id":"10.20944\/preprints201811.0510.v1","asserted-by":"object"}]},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,18]]}}}