{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T04:05:14Z","timestamp":1744344314052,"version":"3.40.4"},"publisher-location":"Singapore","reference-count":49,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819636785"},{"type":"electronic","value":"9789819636792"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-981-96-3679-2_3","type":"book-chapter","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T20:54:54Z","timestamp":1744145694000},"page":"31-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TeleMotion: A Realtime Humanoid Teleoperation System with\u00a0Motion Capture"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7755-5967","authenticated-orcid":false,"given":"Jiabao","family":"Gan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1473-297X","authenticated-orcid":false,"given":"Shihui","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Zhijun","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4797-7188","authenticated-orcid":false,"given":"Xiangren","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,30]]},"reference":[{"key":"3_CR1","unstructured":"Brohan, A., et al.: RT-1: robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817 (2022)"},{"key":"3_CR2","unstructured":"Cheng, X., et al.: Open-television: teleoperation with immersive active visual feedback. arXiv preprint arXiv:2407.01512 (2024)"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Chi, C., et al.: Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots. arXiv preprint arXiv:2402.10329 (2024)","DOI":"10.15607\/RSS.2024.XX.045"},{"key":"3_CR4","unstructured":"Coumans, E., Bai, Y.: Pybullet quickstart guide (2021)"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Dafarra, S., et al.: ICUB3 avatar system: enabling remote fully immersive embodiment of humanoid robots. Sci. Robot. 9(86), eadh3834 (2024)","DOI":"10.1126\/scirobotics.adh3834"},{"issue":"3","key":"3_CR6","doi-asserted-by":"crossref","first-page":"1706","DOI":"10.1109\/TRO.2023.3236952","volume":"39","author":"K Darvish","year":"2023","unstructured":"Darvish, K., et al.: Teleoperation of humanoid robots: a survey. IEEE Trans. Rob. 39(3), 1706\u20131727 (2023)","journal-title":"IEEE Trans. Rob."},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Di\u00a0Fava, A., et al.: Multi-contact motion retargeting from human to humanoid robot. In: 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 1081\u20131086. IEEE (2016)","DOI":"10.1109\/HUMANOIDS.2016.7803405"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Fang, H., et al.: Airexo: low-cost exoskeletons for learning whole-arm manipulation in the wild. In: 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 15031\u201315038. IEEE (2024)","DOI":"10.1109\/ICRA57147.2024.10610799"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Fritsche, L., et al.: First-person tele-operation of a humanoid robot. In: 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 997\u20131002. IEEE (2015)","DOI":"10.1109\/HUMANOIDS.2015.7363475"},{"key":"3_CR10","unstructured":"Fu, Z., Zhao, T.Z., Finn, C.: Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation. arXiv preprint arXiv:2401.02117 (2024)"},{"key":"3_CR11","unstructured":"Fu, Z., et al.: Humanplus: humanoid shadowing and imitation from humans. arXiv preprint arXiv:2406.10454 (2024)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Graves, A., Graves, A.: Long short-term memory. In: Supervised Sequence Labelling with Recurrent Neural Networks, pp. 37\u201345 (2012)","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"He, T., et al.: Learning human-to-humanoid real-time whole-body teleoperation. arXiv preprint arXiv:2403.04436 (2024)","DOI":"10.1109\/IROS58592.2024.10801984"},{"key":"3_CR14","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/ACCESS.2023.3234104","volume":"11","author":"C-K Ho","year":"2023","unstructured":"Ho, C.-K., et al.: A deep learning approach to navigating the joint solution space of redundant inverse kinematics and its applications to numerical IK computations. IEEE Access 11, 2274\u20132290 (2023)","journal-title":"IEEE Access"},{"issue":"6","key":"3_CR15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3272127.3275108","volume":"37","author":"Y Huang","year":"2018","unstructured":"Huang, Y., et al.: Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time. ACM Trans. Graph. (TOG) 37(6), 1\u201315 (2018)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"3_CR16","doi-asserted-by":"crossref","first-page":"6419","DOI":"10.1109\/LRA.2020.3013863","volume":"5","author":"Y Ishiguro","year":"2020","unstructured":"Ishiguro, Y., et al.: Bilateral humanoid teleoperation system using whole-body exoskeleton cockpit tablis. IEEE Robot. Autom. Lett. 5(4), 6419\u20136426 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"3_CR17","unstructured":"Iyer, A., et al.: Open teach: a versatile teleoperation system for robotic manipulation. arXiv preprint arXiv:2403.07870 (2024)"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Jiang, Y., et al.: Transformer inertial poser: real-time human motion reconstruction from sparse IMUs with simultaneous terrain generation. In: SIGGRAPH Asia 2022 Conference Papers, pp. 1\u20139 (2022)","DOI":"10.1145\/3550469.3555428"},{"key":"3_CR19","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.ins.2012.07.051","volume":"222","author":"RI K\u00f6Ker","year":"2013","unstructured":"K\u00f6Ker, R.I.: A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization. Inf. Sci. 222, 528\u2013543 (2013)","journal-title":"Inf. Sci."},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Koubaa, A., et al.: Robot Operating System (ROS), vol. 1. Springer (2017)","DOI":"10.1007\/978-3-319-54927-9"},{"key":"3_CR21","unstructured":"Lejurobot. Kuavo-my. Lejurobot (2024). https:\/\/www.lejurobot.com\/application\/kuavo-my"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Li, S., et al.: A mobile robot hand-arm teleoperation system by vision and IMU. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10900\u201310906. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9340738"},{"key":"3_CR23","unstructured":"Lin, T., et al.: Learning visuotactile skills with two multifingered hands. arXiv preprint arXiv:2404.16823 (2024)"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: A glove-based system for studying hand-object manipulation via joint pose and force sensing. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6617\u20136624. IEEE (2017)","DOI":"10.1109\/IROS.2017.8206575"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Loper, M., et al.: SMPL: a skinned multi-person linear model. In: Seminal Graphics Papers: Pushing the Boundaries, vol. 2, pp. 851\u2013866 (2023)","DOI":"10.1145\/3596711.3596800"},{"issue":"22","key":"3_CR26","doi-asserted-by":"crossref","first-page":"8909","DOI":"10.3390\/s22228909","volume":"22","author":"L Jiaoyang","year":"2022","unstructured":"Jiaoyang, L., Zou, T., Jiang, X.: A neural network based approach to inverse kinematics problem for general six-axis robots. Sensors 22(22), 8909 (2022)","journal-title":"Sensors"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Mahmood, N., et al.: Amass: archive of motion capture as surface shapes. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5442\u20135451 (2019)","DOI":"10.1109\/ICCV.2019.00554"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Otani, K., Bouyarmane, K.: Adaptive whole-body manipulation in human-to-humanoid multi-contact motion retargeting. In: 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pp. 446\u2013453. IEEE (2017)","DOI":"10.1109\/HUMANOIDS.2017.8246911"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Pan, S., et al.: Fusing monocular images and sparse IMU signals for real-time human motion capture. In: SIGGRAPH Asia 2023 Conference Papers, pp. 1\u201311 (2023)","DOI":"10.1145\/3610548.3618145"},{"key":"3_CR30","unstructured":"Park, Y., Agrawal, P.: Using apple vision pro to train and control robots (2024)"},{"issue":"2","key":"3_CR31","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/2856449","volume":"59","author":"B Parno","year":"2016","unstructured":"Parno, B., Howell, J., Gentry, C., Raykova, M.: Pinocchio: nearly practical verifiable computation. Commun. ACM 59(2), 103\u2013112 (2016)","journal-title":"Commun. ACM"},{"key":"3_CR32","unstructured":"Montecillo Puente, F.J., Sreenivasa, M., Laumond, J.P.: On real-time whole-body human to humanoid motion transfer. In: International Conference on Informatics in Control, Automation and Robotics (2010)"},{"key":"3_CR33","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":"3_CR34","doi-asserted-by":"crossref","unstructured":"Ramos, J., Kim, S.: Dynamic locomotion synchronization of bipedal robot and human operator via bilateral feedback teleoperation. Sci. Robot. 4(35), eaav4282 (2019)","DOI":"10.1126\/scirobotics.aav4282"},{"issue":"4","key":"3_CR35","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1109\/TRO.2018.2830387","volume":"34","author":"J Ramos","year":"2018","unstructured":"Ramos, J., Kim, S.: Humanoid dynamic synchronization through whole-body bilateral feedback teleoperation. IEEE Trans. Rob. 34(4), 953\u2013965 (2018)","journal-title":"IEEE Trans. Rob."},{"issue":"8","key":"3_CR36","first-page":"1","volume":"1","author":"M Schepers","year":"2018","unstructured":"Schepers, M., Giuberti, M., Bellusci, G., et al.: Xsens MVN: consistent tracking of human motion using inertial sensing. Xsens Technol. 1(8), 1\u20138 (2018)","journal-title":"Xsens Technol."},{"key":"3_CR37","doi-asserted-by":"crossref","unstructured":"Shah, S.K., Mishra, R., Ray, L.S.: Solution and validation of inverse kinematics using deep artificial neural network. Mater. Today Proc. 26, 1250\u20131254 (2020)","DOI":"10.1016\/j.matpr.2020.02.250"},{"key":"3_CR38","doi-asserted-by":"crossref","unstructured":"Shi, L.X., et al.: Yell at your robot: improving on-the-fly from language corrections. arXiv preprint arXiv:2403.12910 (2024)","DOI":"10.15607\/RSS.2024.XX.025"},{"key":"3_CR39","doi-asserted-by":"crossref","unstructured":"Sivakumar, A., Shaw, K., Pathak, D.: Robotic telekinesis: learning a robotic hand imitator by watching humans on youtube. arXiv preprint arXiv:2202.10448 (2022)","DOI":"10.15607\/RSS.2022.XVIII.023"},{"key":"3_CR40","unstructured":"Octo\u00a0Model Team, et al.: Octo: an open-source generalist robot policy. arXiv preprint arXiv:2405.12213 (2024)"},{"key":"3_CR41","unstructured":"Udexreal. Udexreal. Udexreal (2024). http:\/\/www.udexreal.com\/"},{"key":"3_CR42","doi-asserted-by":"crossref","unstructured":"Van\u00a0Wouwe, T., et al.: Diffusionposer: real-time human motion reconstruction from arbitrary sparse sensors using autoregressive diffusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2513\u20132523 (2024)","DOI":"10.1109\/CVPR52733.2024.00243"},{"key":"3_CR43","doi-asserted-by":"crossref","unstructured":"Wu, P., et al.: Gello: a general, low-cost, and intuitive teleoperation framework for robot manipulators. arXiv preprint arXiv:2309.13037 (2023)","DOI":"10.1109\/IROS58592.2024.10801581"},{"issue":"2","key":"3_CR44","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1109\/LRA.2016.2633383","volume":"2","author":"P-C Yang","year":"2016","unstructured":"Yang, P.-C., Sasaki, K., Suzuki, K., Kase, K., Sugano, S., Ogata, T.: Repeatable folding task by humanoid robot worker using deep learning. IEEE Robot. Autom. Lett. 2(2), 397\u2013403 (2016)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"3_CR45","doi-asserted-by":"crossref","unstructured":"Yi, X., Zhou, Y., Xu, F.: Physical non-inertial poser (PNP): modeling non-inertial effects in sparse-inertial human motion capture. In: ACM SIGGRAPH 2024 Conference Papers, pp. 1\u201311 (2024)","DOI":"10.1145\/3641519.3657436"},{"issue":"4","key":"3_CR46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3450626.3459786","volume":"40","author":"X Yi","year":"2021","unstructured":"Yi, X., Zhou, Y., Feng, X.: Transpose: real-time 3D human translation and pose estimation with six inertial sensors. ACM Trans. Graph. (TOG) 40(4), 1\u201313 (2021)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"3_CR47","doi-asserted-by":"crossref","unstructured":"Yi, X., et al.: Physical inertial poser (PIP): physics-aware real-time human motion tracking from sparse inertial sensors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13167\u201313178 (2022)","DOI":"10.1109\/CVPR52688.2022.01282"},{"key":"3_CR48","doi-asserted-by":"crossref","unstructured":"Zhao, T.Z., et al.: Learning fine-grained bimanual manipulation with low-cost hardware. arXiv preprint arXiv:2304.13705 (2023)","DOI":"10.15607\/RSS.2023.XIX.016"},{"key":"3_CR49","doi-asserted-by":"crossref","unstructured":"Zuo, C., et al.: Loose inertial poser: motion capture with IMU-attached loose-wear jacket. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2209\u20132219 (2024)","DOI":"10.1109\/CVPR52733.2024.00215"}],"container-title":["Lecture Notes in Computer Science","Extended Reality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-3679-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T09:29:11Z","timestamp":1744277351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-3679-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819636785","9789819636792"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-3679-2_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"30 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICXR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Extended Reality","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"14 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icxr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icxr.net\/2024","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}