{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:37:52Z","timestamp":1767339472825,"version":"3.40.4"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031734038"},{"type":"electronic","value":"9783031734045"}],"license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"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\/978-3-031-73404-5_16","type":"book-chapter","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T16:03:13Z","timestamp":1730217793000},"page":"269-285","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning Cross-Hand Policies of\u00a0High-DOF Reaching and\u00a0Grasping"],"prefix":"10.1007","author":[{"given":"Qijin","family":"She","sequence":"first","affiliation":[]},{"given":"Shishun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yunfan","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Ruizhen","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"issue":"4","key":"16_CR1","doi-asserted-by":"publisher","first-page":"62-1","DOI":"10.1145\/3386569.3392462","volume":"39","author":"K Aberman","year":"2020","unstructured":"Aberman, K., Li, P., Lischinski, D., Sorkine-Hornung, O., Cohen-Or, D., Chen, B.: Skeleton-aware networks for deep motion retargeting. ACM Trans. Graph. (TOG) 39(4), 62\u20131 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"16_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3322999","volume":"38","author":"K Aberman","year":"2019","unstructured":"Aberman, K., Wu, R., Lischinski, D., Chen, B., Cohen-Or, D.: Learning character-agnostic motion for motion retargeting in 2D. ACM Trans. Graph. (TOG) 38(4), 1\u201314 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Brahmbhatt, S., Handa, A., Hays, J., Fox, D.: ContactGrasp: functional multi-finger grasp synthesis from contact. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2386\u20132393. IEEE (2019)","DOI":"10.1109\/IROS40897.2019.8967960"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/978-3-030-58601-0_22","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Brahmbhatt","year":"2020","unstructured":"Brahmbhatt, S., Tang, C., Twigg, C.D., Kemp, C.C., Hays, J.: ContactPose: a dataset of grasps with object contact and hand pose. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, pp. 361\u2013378. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58601-0_22"},{"issue":"3","key":"16_CR5","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1177\/0278364917700714","volume":"36","author":"B Calli","year":"2017","unstructured":"Calli, B., et al.: Yale-CMU-Berkeley dataset for robotic manipulation research. Int. J. Robot. Res. 36(3), 261\u2013268 (2017)","journal-title":"Int. J. Robot. Res."},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Chen, L.Y., Hari, K., Dharmarajan, K., Xu, C., Vuong, Q., Goldberg, K.: Mirage: cross-embodiment zero-shot policy transfer with cross-painting. arXiv preprint arXiv:2402.19249 (2024)","DOI":"10.15607\/RSS.2024.XX.069"},{"key":"16_CR7","unstructured":"Chen, T., Murali, A., Gupta, A.: Hardware conditioned policies for multi-robot transfer learning. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"key":"16_CR8","unstructured":"Coumans, E., Bai, Y.: A python module for physics simulation for games, robotics and machine learning (2021). PyBullet. http:\/\/pybullet.org"},{"key":"16_CR9","unstructured":"Unreal Engine: IK Rig in unreal engine. Technical report (2021). https:\/\/docs.unrealengine.com\/5.0\/en-US\/ik-rig-in-unreal-engine\/"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Fang, H.S., et al.: AnyGrasp: robust and efficient grasp perception in spatial and temporal domains. IEEE Trans. Robot. 39(5) (2023)","DOI":"10.1109\/TRO.2023.3281153"},{"key":"16_CR11","unstructured":"Gupta, A., Fan, L., Ganguli, S., Fei-Fei, L.: MetaMorph: learning universal controllers with transformers. In: International Conference on Learning Representations (2022)"},{"key":"16_CR12","unstructured":"Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: International Conference on Machine Learning, pp. 1861\u20131870. PMLR (2018)"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Handa, A., et al.: DexPilot: vision-based teleoperation of dexterous robotic hand-arm system. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 9164\u20139170. IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9197124"},{"key":"16_CR14","unstructured":"Hong, S., Yoon, D., Kim, K.E.: Structure-aware transformer policy for inhomogeneous multi-task reinforcement learning. In: International Conference on Learning Representations (2021)"},{"key":"16_CR15","unstructured":"Huang, W., Mordatch, I., Pathak, D.: One policy to control them all: Shared modular policies for agent-agnostic control. In: International Conference on Machine Learning, pp. 4455\u20134464. PMLR (2020)"},{"key":"16_CR16","unstructured":"Kalashnikov, D., et al.: QT-Opt: scalable deep reinforcement learning for vision-based robotic manipulation. In: Robotics Science and Systems (RSS) (2018)"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Kappler, D., Bohg, J., Schaal, S.: Leveraging big data for grasp planning. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4304\u20134311. IEEE (2015)","DOI":"10.1109\/ICRA.2015.7139793"},{"issue":"8","key":"16_CR18","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1177\/0278364912445831","volume":"31","author":"A Kasper","year":"2012","unstructured":"Kasper, A., Xue, Z., Dillmann, R.: The kit object models database: an object model database for object recognition, localization and manipulation in service robotics. Int. J. Robot. Res. 31(8), 927\u2013934 (2012)","journal-title":"Int. J. Robot. Res."},{"key":"16_CR19","unstructured":"Khargonkar, N., Song, N., Xu, Z., Prabhakaran, B., Xiang, Y.: NeuralGrasps: learning implicit representations for grasps of multiple robotic hands. In: Conference on Robot Learning, pp. 516\u2013526. PMLR (2023)"},{"key":"16_CR20","unstructured":"Kurin, V., Igl, M., Rockt\u00e4schel, T., Boehmer, W., Whiteson, S.: My body is a cage: the role of morphology in graph-based incompatible control. In: ICLR 2021 (2021)"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Lee, J., Shin, S.Y.: A hierarchical approach to interactive motion editing for human-like figures. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 39\u201348 (1999)","DOI":"10.1145\/311535.311539"},{"issue":"4","key":"16_CR22","doi-asserted-by":"publisher","first-page":"8619","DOI":"10.1109\/LRA.2022.3187875","volume":"7","author":"K Li","year":"2022","unstructured":"Li, K., Baron, N., Zhang, X., Rojas, N.: EfficientGrasp: a unified data-efficient learning to grasp method for multi-fingered robot hands. IEEE Robot. Autom. Lett. 7(4), 8619\u20138626 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Li, P., et al.: GenDexGrasp: generalizable dexterous grasping. In: 2022 International Conference on Robotics and Automation (ICRA) (2022)","DOI":"10.1109\/ICRA48891.2023.10160667"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Liu, M., Pan, Z., Xu, K., Ganguly, K., Manocha, D.: Deep differentiable grasp planner for high-DOF grippers. In: Proceedings of the Robotics: Science and Systems 2020 (2020)","DOI":"10.15607\/RSS.2020.XVI.066"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Mahler, J., Matl, M., Liu, X., Li, A., Gealy, D., Goldberg, K.: Dex-Net 3.0: computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 5620\u20135627. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8460887"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Mandikal, P., Grauman, K.: Learning dexterous grasping with object-centric visual affordances. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 6169\u20136176. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561802"},{"key":"16_CR27","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":"4","key":"16_CR28","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/MRA.2004.1371616","volume":"11","author":"AT Miller","year":"2004","unstructured":"Miller, A.T., Allen, P.K.: Graspit! A versatile simulator for robotic grasping. IEEE Robot. Autom. Mag. 11(4), 110\u2013122 (2004)","journal-title":"IEEE Robot. Autom. Mag."},{"key":"16_CR29","unstructured":"Pathak, D., Lu, C., Darrell, T., Isola, P., Efros, A.A.: Learning to control self-assembling morphologies: a study of generalization via modularity. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Penco, L., et al.: Robust real-time whole-body motion retargeting from human to humanoid. In: 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), pp. 425\u2013432. IEEE (2018)","DOI":"10.1109\/HUMANOIDS.2018.8624943"},{"key":"16_CR31","unstructured":"Peng, X.B., Coumans, E., Zhang, T., Lee, T.W., Tan, J., Levine, S.: Learning agile robotic locomotion skills by imitating animals. In: Proceedings of the Robotics: Science and Systems (2020)"},{"issue":"6","key":"16_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3272127.3275014","volume":"37","author":"XB Peng","year":"2018","unstructured":"Peng, X.B., Kanazawa, A., Malik, J., Abbeel, P., Levine, S.: SFV: reinforcement learning of physical skills from videos. ACM Trans. Graph. (TOG) 37(6), 1\u201314 (2018)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"16_CR33","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)"},{"issue":"4","key":"16_CR34","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":"16_CR35","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\u00a0dexterous manipulation from\u00a0human videos. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, pp. 570\u2013587. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19842-7_33"},{"key":"16_CR36","doi-asserted-by":"crossref","unstructured":"Qin, Y., et al.: AnyTeleop: a general vision-based dexterous robot arm-hand teleoperation system. In: Robotics: Science and Systems (2023)","DOI":"10.15607\/RSS.2023.XIX.015"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Romero, J., Tzionas, D., Black, M.J.: Embodied hands: modeling and capturing hands and bodies together. ACM Trans. Graph. Proc. SIGGRAPH Asia 36(6) (2017)","DOI":"10.1145\/3130800.3130883"},{"issue":"1","key":"16_CR38","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Schaff, C., Yunis, D., Chakrabarti, A., Walter, M.R.: Jointly learning to construct and control agents using deep reinforcement learning. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 9798\u20139805. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8793537"},{"issue":"2","key":"16_CR40","doi-asserted-by":"publisher","first-page":"2286","DOI":"10.1109\/LRA.2020.2969946","volume":"5","author":"L Shao","year":"2020","unstructured":"Shao, L., et al.: UniGrasp: learning a unified model to grasp with multifingered robotic hands. IEEE Robot. Autom. Lett. 5(2), 2286\u20132293 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"She, Q., Hu, R., Xu, J., Liu, M., Xu, K., Huang, H.: Learning high-DOF reaching-and-grasping via dynamic representation of gripper-object interaction. ACM Trans. Graph. SIGGRAPH 2022 41(4) (2022)","DOI":"10.1145\/3528223.3530091"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Sivakumar, A., Shaw, K., Pathak, D.: Robotic telekinesis: learning a robotic hand imitator by watching humans on YouTube. In: Robotics: Science and Systems (RSS) (2022)","DOI":"10.15607\/RSS.2022.XVIII.023"},{"key":"16_CR43","doi-asserted-by":"publisher","unstructured":"Turpin, D., et al.: Grasp\u2019D: differentiable contact-rich grasp synthesis for multi-fingered hands. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision\u2013ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol. 13666, pp. 201\u2013221. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20068-7_12","DOI":"10.1007\/978-3-031-20068-7_12"},{"key":"16_CR44","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Villegas, R., Yang, J., Ceylan, D., Lee, H.: Neural kinematic networks for unsupervised motion retargetting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8639\u20138648 (2018)","DOI":"10.1109\/CVPR.2018.00901"},{"key":"16_CR46","unstructured":"Wang, T., Liao, R., Ba, J., Fidler, S.: NerveNet: learning structured policy with graph neural networks. In: Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada, vol.\u00a030 (2018)"},{"key":"16_CR47","doi-asserted-by":"crossref","unstructured":"Xu, Z., Qi, B., Agrawal, S., Song, S.: AdaGrasp: learning an adaptive gripper-aware grasping policy. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 4620\u20134626. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9560833"},{"key":"16_CR48","unstructured":"Yang, J., Sadigh, D., Finn, C.: Polybot: training one policy across robots while embracing variability. arXiv preprint arXiv:2307.03719 (2023)"},{"issue":"3","key":"16_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2574860","volume":"33","author":"X Zhao","year":"2014","unstructured":"Zhao, X., Wang, H., Komura, T.: Indexing 3D scenes using the interaction bisector surface. ACM Trans. Graph. (TOG) 33(3), 1\u201314 (2014)","journal-title":"ACM Trans. Graph. (TOG)"}],"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\/978-3-031-73404-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T19:45:01Z","timestamp":1745523901000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73404-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"ISBN":["9783031734038","9783031734045"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73404-5_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,30]]},"assertion":[{"value":"30 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"}}]}}