{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:35:22Z","timestamp":1784644522303,"version":"3.55.0"},"publisher-location":"Cham","reference-count":72,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732287","type":"print"},{"value":"9783031732294","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73229-4_10","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T15:03:09Z","timestamp":1729782189000},"page":"164-182","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Parameterized Quasi-Physical Simulators for\u00a0Dexterous Manipulations Transfer"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7494-6807","authenticated-orcid":false,"given":"Xueyi","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangbo","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieqiong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9319-0354","authenticated-orcid":false,"given":"Li","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Ajay, A., et al.: Augmenting physical simulators with stochastic neural networks: case study of planar pushing and bouncing. In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3066\u20133073. IEEE (2018)","DOI":"10.1109\/IROS.2018.8593995"},{"key":"10_CR2","unstructured":"Akkaya, I., et al.: Solving Rubik\u2019s cube with a robot hand. arXiv preprint arXiv:1910.07113 (2019)"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Andrews, S., Erleben, K., Ferguson, Z.: Contact and friction simulation for computer graphics. In: ACM SIGGRAPH 2022 Courses, pp. 1\u2013172 (2022)","DOI":"10.1145\/3532720.3535640"},{"issue":"1","key":"10_CR4","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/0278364919887447","volume":"39","author":"OM Andrychowicz","year":"2020","unstructured":"Andrychowicz, O.M., et al.: Learning dexterous in-hand manipulation. Int. J. Robot. Res. 39(1), 3\u201320 (2020)","journal-title":"Int. J. Robot. Res."},{"key":"10_CR5","unstructured":"Baraff, D.: An introduction to physically based modeling: rigid body simulation II\u2014nonpenetration constraints In: SIGGRAPH Course Notes, pp. D31\u2013D68 (1997)"},{"key":"10_CR6","doi-asserted-by":"publisher","unstructured":"Chen, T., Tippur, M., Wu, S., Kumar, V., Adelson, E., Agrawal, P.: Visual dexterity: in-hand reorientation of novel and complex object shapes. Sci. Robot. 8(84), eadc9244 (2023). https:\/\/doi.org\/10.1126\/scirobotics.adc9244","DOI":"10.1126\/scirobotics.adc9244"},{"key":"10_CR7","unstructured":"Chen, T., Xu, J., Agrawal, P.: A system for general in-hand object re-orientation. In: Conference on Robot Learning (2021)"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Christen, S., Kocabas, M., Aksan, E., Hwangbo, J., Song, J., Hilliges, O.: D-grasp: physically plausible dynamic grasp synthesis for hand-object interactions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20577\u201320586 (2022)","DOI":"10.1109\/CVPR52688.2022.01992"},{"key":"10_CR9","unstructured":"Coumans, E., Bai, Y.: Pybullet, a python module for physics simulation for games, robotics and machine learning (2016)"},{"key":"10_CR10","unstructured":"Deng, Y., Yu, H.X., Wu, J., Zhu, B.: Learning vortex dynamics for fluid inference and prediction. arXiv preprint arXiv:2301.11494 (2023)"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Du, T.: Deep learning for physics simulation. In: ACM SIGGRAPH 2023 Courses, pp. 1\u201325 (2023)","DOI":"10.1145\/3587423.3595518"},{"key":"10_CR12","unstructured":"Dunlavy, D.M., O\u2019Leary, D.P.: Homotopy optimization methods for global optimization. Technical report, Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (2005)"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Fan, Z., et al.: ARCTIC: a dataset for dexterous bimanual hand-object manipulation. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2023)","DOI":"10.1109\/CVPR52729.2023.01244"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Featherstone, R.: Rigid body dynamics algorithms (2007). https:\/\/api.semanticscholar.org\/CorpusID:58437819","DOI":"10.1007\/978-1-4899-7560-7"},{"key":"10_CR15","unstructured":"Freeman, C.D., Frey, E., Raichuk, A., Girgin, S., Mordatch, I., Bachem, O.: Brax\u2013a differentiable physics engine for large scale rigid body simulation. arXiv preprint arXiv:2106.13281 (2021)"},{"issue":"6","key":"10_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3478513.3480527","volume":"40","author":"L Fussell","year":"2021","unstructured":"Fussell, L., Bergamin, K., Holden, D.: Supertrack: motion tracking for physically simulated characters using supervised learning. ACM Trans. Graph. (TOG) 40(6), 1\u201313 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Gao, J., Michelis, M.Y., Spielberg, A., Katzschmann, R.K.: Sim-to-real of soft robots with learned residual physics. arXiv preprint arXiv:2402.01086 (2024)","DOI":"10.1109\/LRA.2024.3446287"},{"issue":"3","key":"10_CR18","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/0005-1098(89)90002-2","volume":"25","author":"CE Garcia","year":"1989","unstructured":"Garcia, C.E., Prett, D.M., Morari, M.: Model predictive control: theory and practice\u2013a survey. Automatica 25(3), 335\u2013348 (1989)","journal-title":"Automatica"},{"issue":"6","key":"10_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3414685.3417766","volume":"39","author":"M Geilinger","year":"2020","unstructured":"Geilinger, M., Hahn, D., Zehnder, J., B\u00e4cher, M., Thomaszewski, B., Coros, S.: ADD: analytically differentiable dynamics for multi-body systems with frictional contact. ACM Trans. Graph. (TOG) 39(6), 1\u201315 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"10_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3592454","volume":"42","author":"R Grandia","year":"2023","unstructured":"Grandia, R., Farshidian, F., Knoop, E., Schumacher, C., Hutter, M., B\u00e4cher, M.: DOC: differentiable optimal control for retargeting motions onto legged robots. ACM Trans. Graph. (TOG) 42(4), 1\u201314 (2023)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Gupta, A., Eppner, C., Levine, S., Abbeel, P.: Learning dexterous manipulation for a soft robotic hand from human demonstrations. In: 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3786\u20133793. IEEE (2016)","DOI":"10.1109\/IROS.2016.7759557"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Heiden, E., Millard, D., Coumans, E., Sheng, Y., Sukhatme, G.S.: Neuralsim: Augmenting differentiable simulators with neural networks. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). pp. 9474\u20139481. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9560935"},{"key":"10_CR23","unstructured":"Howell, T.A., Le\u00a0Cleac\u2019h, S., Kolter, J.Z., Schwager, M., Manchester, Z.: Dojo: A differentiable simulator for robotics. arXiv preprint arXiv:2203.008069 (2022)"},{"issue":"3","key":"10_CR24","doi-asserted-by":"publisher","first-page":"6750","DOI":"10.1109\/LRA.2022.3152696","volume":"7","author":"TA Howell","year":"2022","unstructured":"Howell, T.A., Le Cleac\u2019h, S., Singh, S., Florence, P., Manchester, Z., Sindhwani, V.: Trajectory optimization with optimization-based dynamics. IEEE Robot. Autom. Lett. 7(3), 6750\u20136757 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"2","key":"10_CR25","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1109\/LRA.2018.2792536","volume":"3","author":"J Hwangbo","year":"2018","unstructured":"Hwangbo, J., Lee, J., Hutter, M.: Per-contact iteration method for solving contact dynamics. IEEE Robot. Autom. Lett. 3(2), 895\u2013902 (2018)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Lan, L., Yang, Y., Kaufman, D., Yao, J., Li, M., Jiang, C.: Medial IPC: accelerated incremental potential contact with medial elastics. ACM Trans. Graph. 40(4) (2021)","DOI":"10.1145\/3476576.3476741"},{"issue":"4","key":"10_CR27","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/3386569.3392425","volume":"39","author":"M Li","year":"2020","unstructured":"Li, M., et al.: Incremental potential contact: intersection-and inversion-free, large-deformation dynamics. ACM Trans. Graph. 39(4), 49 (2020)","journal-title":"ACM Trans. Graph."},{"issue":"2","key":"10_CR28","first-page":"499","volume":"147","author":"S Liao","year":"2004","unstructured":"Liao, S.: On the homotopy analysis method for nonlinear problems. Appl. Math. Comput. 147(2), 499\u2013513 (2004)","journal-title":"Appl. Math. Comput."},{"key":"10_CR29","unstructured":"Lin, X., Yang, Z., Zhang, X., Zhang, Q.: Continuation path learning for homotopy optimization (2023)"},{"key":"10_CR30","unstructured":"Liu, C.K., Jain, S.: A quick tutorial on multibody dynamics. Online Tutorial, 7 (2012)"},{"key":"10_CR31","unstructured":"Liu, X., Pathak, D., Kitani, K.M.: HERD: continuous human-to-robot evolution for learning from human demonstration. arXiv preprint arXiv:2212.04359 (2022)"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: TACO: benchmarking generalizable bimanual tool-action-object understanding. arXiv preprint arXiv:2401.08399 (2024)","DOI":"10.1109\/CVPR52733.2024.02054"},{"key":"10_CR33","unstructured":"Makoviychuk, V., et al.: Isaac gym: High performance GPU-based physics simulation for robot learning. arXiv preprint arXiv:2108.10470 (2021)"},{"key":"10_CR34","unstructured":"Mandikal, P., Grauman, K.: DexVIP: learning dexterous grasping with human hand pose priors from video. In: Conference on Robot Learning, pp. 651\u2013661. PMLR (2022)"},{"issue":"1","key":"10_CR35","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1109\/LRA.2016.2547024","volume":"2","author":"T Marcucci","year":"2016","unstructured":"Marcucci, T., Gabiccini, M., Artoni, A.: A two-stage trajectory optimization strategy for articulated bodies with unscheduled contact sequences. IEEE Robot. Autom. Lett. 2(1), 104\u2013111 (2016)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10_CR36","unstructured":"Mordatch, I., Popovi\u0107, Z., Todorov, E.: Contact-invariant optimization for hand manipulation. In: Proceedings of the ACM SIGGRAPH\/Eurographics Symposium on Computer Animation, pp. 137\u2013144 (2012)"},{"key":"10_CR37","doi-asserted-by":"crossref","unstructured":"Pang, T., Suh, H.T., Yang, L., Tedrake, R.: Global planning for contact-rich manipulation via local smoothing of quasi-dynamic contact models. IEEE Trans. Robot. (2023)","DOI":"10.1109\/TRO.2023.3300230"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Pang, T., Tedrake, R.: A convex quasistatic time-stepping scheme for rigid multibody systems with contact and friction. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 6614\u20136620. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9560941"},{"issue":"4","key":"10_CR39","first-page":"1","volume":"37","author":"XB Peng","year":"2018","unstructured":"Peng, X.B., Abbeel, P., Levine, S., Van de Panne, M.: DeepMimic: example-guided deep reinforcement learning of physics-based character skills. ACM Trans. Graph. (TOG) 37(4), 1\u201314 (2018)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"10_CR40","unstructured":"Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., Battaglia, P.W.: Learning mesh-based simulation with graph networks. arXiv preprint arXiv:2010.03409 (2020)"},{"key":"10_CR41","unstructured":"Pfrommer, S., Halm, M., Posa, M.: ContactNets: learning discontinuous contact dynamics with smooth, implicit representations. In: Conference on Robot Learning, pp. 2279\u20132291. PMLR (2021)"},{"issue":"4","key":"10_CR42","doi-asserted-by":"publisher","first-page":"10873","DOI":"10.1109\/LRA.2022.3196104","volume":"7","author":"Y Qin","year":"2022","unstructured":"Qin, Y., Su, H., Wang, X.: From one hand to multiple hands: imitation learning for dexterous manipulation from single-camera teleoperation. IEEE Robot. Autom. Lett. 7(4), 10873\u201310881 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1007\/978-3-031-19842-7_33","volume-title":"Computer Vision \u2013 ECCV 2022","author":"Y Qin","year":"2022","unstructured":"Qin, Y., et al.: DexMV: imitation learning for dexterous manipulation from human videos. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13699, pp. 570\u2013587. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19842-7_33"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Qin, Y., et al.: AnyTeleop: a general vision-based dexterous robot arm-hand teleoperation system. arXiv preprint arXiv:2307.04577 (2023)","DOI":"10.15607\/RSS.2023.XIX.015"},{"key":"10_CR45","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Wang, X., Pinto, L., Malik, J.: State-only imitation learning for dexterous manipulation. In: 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7865\u20137871. IEEE (2021)","DOI":"10.1109\/IROS51168.2021.9636557"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Rajeswaran, A., et al.: Learning complex dexterous manipulation with deep reinforcement learning and demonstrations. arXiv preprint arXiv:1709.10087 (2017)","DOI":"10.15607\/RSS.2018.XIV.049"},{"key":"10_CR47","unstructured":"Rusu, A.A., et al.: Policy distillation. arXiv preprint arXiv:1511.06295 (2015)"},{"key":"10_CR48","unstructured":"Schmeckpeper, K., Rybkin, O., Daniilidis, K., Levine, S., Finn, C.: Reinforcement learning with videos: Combining offline observations with interaction. arXiv preprint arXiv:2011.06507 (2020)"},{"key":"10_CR49","unstructured":"ShadowRobot: Shadowrobot dexterous hand (2005). https:\/\/www.shadowrobot.com\/dexterous-hand-series\/"},{"key":"10_CR50","unstructured":"Siahkoohi, A., Louboutin, M., Herrmann, F.J.: Neural network augmented wave-equation simulation. arXiv preprint arXiv:1910.00925 (2019)"},{"key":"10_CR51","unstructured":"Suh, H., Wang, Y.: Comparing effectiveness of relaxation methods for warm starting trajectory optimization through soft contact (2019)"},{"issue":"2","key":"10_CR52","doi-asserted-by":"publisher","first-page":"4000","DOI":"10.1109\/LRA.2022.3146931","volume":"7","author":"HJT Suh","year":"2022","unstructured":"Suh, H.J.T., Pang, T., Tedrake, R.: Bundled gradients through contact via randomized smoothing. IEEE Robot. Autom. Lett. 7(2), 4000\u20134007 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10_CR53","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/978-3-030-58548-8_34","volume-title":"Computer Vision \u2013 ECCV 2020","author":"O Taheri","year":"2020","unstructured":"Taheri, O., Ghorbani, N., Black, M.J., Tzionas, D.: GRAB: a dataset of whole-body human grasping of objects. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 581\u2013600. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_34"},{"key":"10_CR54","unstructured":"Tan, K.K., Wang, Q.G., Hang, C.C.: Advances in PID Control. Springer, Berlin (2012)"},{"key":"10_CR55","unstructured":"Tedrake, R.: The Drake Development\u00a0Team: Drake: Model-based Design and Verification for Robotics (2019). https:\/\/drake.mit.edu"},{"key":"10_CR56","doi-asserted-by":"crossref","unstructured":"Todorov, E., Erez, T., Tassa, Y.: MuJoCo: a physics engine for model-based control. In: 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 5026\u20135033. IEEE (2012)","DOI":"10.1109\/IROS.2012.6386109"},{"key":"10_CR57","unstructured":"Traor\u00e9, R., et al.: Continual reinforcement learning deployed in real-life using policy distillation and sim2real transfer. arXiv preprint arXiv:1906.04452 (2019)"},{"key":"10_CR58","doi-asserted-by":"crossref","unstructured":"Villegas, R., Ceylan, D., Hertzmann, A., Yang, J., Saito, J.: Contact-aware retargeting of skinned motion. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9720\u20139729 (2021)","DOI":"10.1109\/ICCV48922.2021.00958"},{"issue":"4","key":"10_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3322941","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Weidner, N.J., Baxter, M.A., Hwang, Y., Kaufman, D.M., Sueda, S.: REDMAX: efficient & flexible approach for articulated dynamics. ACM Trans. Graph. (TOG) 38(4), 1\u201310 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"10_CR60","unstructured":"Wang, Y., Lin, J., Zeng, A., Luo, Z., Zhang, J., Zhang, L.: PhysHOI: physics-based imitation of dynamic human-object interaction. arXiv preprint arXiv:2312.04393 (2023)"},{"issue":"3","key":"10_CR61","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/0045-7825(89)90053-4","volume":"74","author":"LT Watson","year":"1989","unstructured":"Watson, L.T., Haftka, R.T.: Modern homotopy methods in optimization. Comput. Meth. Appl. Mech. Eng. 74(3), 289\u2013305 (1989)","journal-title":"Comput. Meth. Appl. Mech. Eng."},{"key":"10_CR62","unstructured":"Wu, Y.H., Wang, J., Wang, X.: Learning generalizable dexterous manipulation from human grasp affordance. In: Conference on Robot Learning, pp. 618\u2013629. PMLR (2023)"},{"key":"10_CR63","doi-asserted-by":"publisher","first-page":"105811","DOI":"10.1016\/j.compfluid.2023.105811","volume":"258","author":"S Xiong","year":"2023","unstructured":"Xiong, S., He, X., Tong, Y., Deng, Y., Zhu, B.: Neural vortex method: from finite lagrangian particles to infinite dimensional Eulerian dynamics. Comput. Fluids 258, 105811 (2023)","journal-title":"Comput. Fluids"},{"key":"10_CR64","doi-asserted-by":"crossref","unstructured":"Xu, J., et al.: An end-to-end differentiable framework for contact-aware robot design. arXiv preprint arXiv:2107.07501 (2021)","DOI":"10.15607\/RSS.2021.XVII.008"},{"key":"10_CR65","doi-asserted-by":"crossref","unstructured":"Yamane, K., Nakamura, Y.: Stable penalty-based model of frictional contacts. In: Proceedings 2006 IEEE International Conference on Robotics and Automation. ICRA 2006, pp. 1904\u20131909. IEEE (2006)","DOI":"10.1109\/ROBOT.2006.1641984"},{"issue":"6","key":"10_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3550454.3555434","volume":"41","author":"H Yao","year":"2022","unstructured":"Yao, H., Song, Z., Chen, B., Liu, L.: ControlVAE: model-based learning of generative controllers for physics-based characters. ACM Trans. Graph. (TOG) 41(6), 1\u201316 (2022)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"10_CR67","doi-asserted-by":"publisher","first-page":"1307","DOI":"10.1109\/TRO.2020.2988642","volume":"36","author":"A Zeng","year":"2020","unstructured":"Zeng, A., Song, S., Lee, J., Rodriguez, A., Funkhouser, T.: TossingBot: learning to throw arbitrary objects with residual physics. IEEE Trans. Rob. 36(4), 1307\u20131319 (2020)","journal-title":"IEEE Trans. Rob."},{"key":"10_CR68","unstructured":"Zhang, H., et al.: ArtiGrasp: physically plausible synthesis of bi-manual dexterous grasping and articulation. arXiv preprint arXiv:2309.03891 (2023)"},{"key":"10_CR69","unstructured":"Zhang, S., Liu, B., Wang, Z., Zhao, T.: Model-based reparameterization policy gradient methods: theory and practical algorithms. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"10_CR70","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Clegg, A., Ha, S., Turk, G., Ye, Y.: Learning to transfer in-hand manipulations using a greedy shape curriculum. In: Computer Graphics Forum, vol. 42, pp. 25\u201336. Wiley (2023)","DOI":"10.1111\/cgf.14741"},{"key":"10_CR71","doi-asserted-by":"crossref","unstructured":"Zhao, W., Queralta, J.P., Westerlund, T.: Sim-to-real transfer in deep reinforcement learning for robotics: a survey. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 737\u2013744. IEEE (2020)","DOI":"10.1109\/SSCI47803.2020.9308468"},{"issue":"1","key":"10_CR72","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43\u201376 (2020)","journal-title":"Proc. IEEE"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73229-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T15:07:12Z","timestamp":1729782432000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73229-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031732287","9783031732294"],"references-count":72,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73229-4_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}