{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:02:17Z","timestamp":1742918537589,"version":"3.40.3"},"publisher-location":"Cham","reference-count":69,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031227301"},{"type":"electronic","value":"9783031227318"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-22731-8_16","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T11:08:15Z","timestamp":1672571295000},"page":"218-232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Toward Human-Robot Cooperation: Unsupervised Domain Adaptation for\u00a0Egocentric Action Recognition"],"prefix":"10.1007","author":[{"given":"Mirco","family":"Planamente","sequence":"first","affiliation":[]},{"given":"Gabriele","family":"Goletto","sequence":"additional","affiliation":[]},{"given":"Gabriele","family":"Trivigno","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Averta","sequence":"additional","affiliation":[]},{"given":"Barbara","family":"Caputo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"16_CR1","unstructured":"Agarwal, N., Chen, Y.T., Dariush, B., Yang, M.H.: Unsupervised domain adaptation for spatio-temporal action localization. arXiv preprint arXiv:2010.09211 (2020)"},{"issue":"5","key":"16_CR2","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1007\/s10514-017-9677-2","volume":"42","author":"A Ajoudani","year":"2018","unstructured":"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Sch\u00e4ffer, A., Kosuge, K., Khatib, O.: Progress and prospects of the human-robot collaboration. Autonom. Rob. 42(5), 957\u2013975 (2018)","journal-title":"Autonom. Rob."},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Bucci, S., D\u2019Innocente, A., Liao, Y., Carlucci, F.M., Caputo, B., Tommasi, T.: Self-supervised learning across domains (2020)","DOI":"10.1109\/TPAMI.2021.3070791"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Carlucci, F.M., D\u2019Innocente, A., Bucci, S., Caputo, B., Tommasi, T.: Domain generalization by solving jigsaw puzzles. In: CVPR, pp. 2229\u20132238 (2019)","DOI":"10.1109\/CVPR.2019.00233"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: CVPR, pp. 6299\u20136308 (2017)","DOI":"10.1109\/CVPR.2017.502"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Cartas, A., Luque, J., Radeva, P., Segura, C., Dimiccoli, M.: Seeing and hearing egocentric actions: how much can we learn? In: ICCV Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00548"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Chang, W.G., You, T., Seo, S., Kwak, S., Han, B.: Domain-specific batch normalization for unsupervised domain adaptation. In: CVPR, pp. 7354\u20137362 (2019)","DOI":"10.1109\/CVPR.2019.00753"},{"key":"16_CR8","unstructured":"Chen, H.Y., Wang, P.H., Liu, C.H., Chang, S.C., Pan, J.Y., Chen, Y.T., Wei, W., Juan, D.C.: Complement objective training. arXiv preprint arXiv:1903.01182 (2019)"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Chen, M.H., Kira, Z., AlRegib, G., Yoo, J., Chen, R., Zheng, J.: Temporal attentive alignment for large-scale video domain adaptation. In: ICCV, pp. 6321\u20136330 (2019)","DOI":"10.1109\/ICCV.2019.00642"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Chen, M.H., Li, B., Bao, Y., AlRegib, G., Kira, Z.: Action segmentation with joint self-supervised temporal domain adaptation. In: CVPR, pp. 9454\u20139463 (2020)","DOI":"10.1109\/CVPR42600.2020.00947"},{"key":"16_CR11","unstructured":"Cheng, Y., Fang, F., Sun, Y.: Team vi-i2r technical report on epic-kitchens-100 unsupervised domain adaptation challenge for action recognition 2021. arXiv preprint arXiv:2206.02573 (2022)"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Choi, J., Sharma, G., Chandraker, M., Huang, J.B.: Unsupervised and semi-supervised domain adaptation for action recognition from drones. In: WACV, pp. 1717\u20131726 (2020)","DOI":"10.1109\/WACV45572.2020.9093511"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Choi, J., Sharma, G., Schulter, S., Huang, J.B.: Shuffle and attend: Video domain adaptation. In: ECCV, pp. 678\u2013695. Springer (2020)","DOI":"10.1007\/978-3-030-58610-2_40"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Damen, D., Doughty, H., Farinella, G.M., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Munro, J., Perrett, T., Price, W., Wray, M.: Scaling egocentric vision: the epic-kitchens dataset (2018)","DOI":"10.1007\/978-3-030-01225-0_44"},{"key":"16_CR15","unstructured":"Damen, D., Doughty, H., Farinella, G.M., Furnari, A., Kazakos, E., Ma, J., Moltisanti, D., Munro, J., Perrett, T., Price, W., et\u00a0al.: Rescaling egocentric vision. arXiv preprint arXiv:2006.13256 (2020)"},{"key":"16_CR16","unstructured":"Damen, D., Kazakos, E., Price, W., Ma, J., Doughty, H.: Epic-kitchens-55\u20142020 challenges report (2020)"},{"key":"16_CR17","unstructured":"Damen, D., Price, W., Kazakos, E., Furnari, A., Farinella, G.M.: Epic-kitchens\u20142019 challenges report (2019)"},{"key":"16_CR18","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Kai Li, Li Fei-Fei: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Deng, Z., Luo, Y., Zhu, J.: Cluster alignment with a teacher for unsupervised domain adaptation. In: ICCV, pp. 9944\u20139953 (2019)","DOI":"10.1109\/ICCV.2019.01004"},{"key":"16_CR20","unstructured":"Dou, Q., Coelho de Castro, D., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. NeurIPS 32, 6450\u20136461 (2019)"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Furnari, A., Farinella, G.: Rolling-unrolling LSTMS for action anticipation from first-person video. T-PAMI (2020)","DOI":"10.1109\/TPAMI.2020.2992889"},{"key":"16_CR22","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML, pp. 1180\u20131189. PMLR (2015)"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Ghadiyaram, D., Tran, D., Mahajan, D.: Large-scale weakly-supervised pre-training for video action recognition. In: CVPR, pp. 12,046\u201312,055 (2019)","DOI":"10.1109\/CVPR.2019.01232"},{"key":"16_CR24","unstructured":"Gibson, J.J.: The theory of affordances. Hilldale, USA 1(2), 67\u201382 (1977)"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"6","key":"16_CR26","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/j.tins.2020.03.013","volume":"43","author":"A Henschel","year":"2020","unstructured":"Henschel, A., Hortensius, R., Cross, E.S.: Social cognition in the age of human-robot interaction. Trends Neurosci 43(6), 373\u2013384 (2020)","journal-title":"Trends Neurosci"},{"key":"16_CR27","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: F.\u00a0Bach, D.\u00a0Blei (eds.) ICML, Proceedings of Machine Learning Research, vol.\u00a037, pp. 448\u2013456. PMLR (2015). URL http:\/\/proceedings.mlr.press\/v37\/ioffe15.html"},{"key":"16_CR28","unstructured":"Jamal, A., Namboodiri, V.P., Deodhare, D., Venkatesh, K.: Deep domain adaptation in action space. In: BMVC, vol.\u00a02, p.\u00a05 (2018)"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Kapidis, G., Poppe, R., van Dam, E., Noldus, L., Veltkamp, R.: Multitask learning to improve egocentric action recognition. In: ICCV Workshops, pp. 0\u20130 (2019)","DOI":"10.1109\/ICCVW.2019.00540"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Kazakos, E., Nagrani, A., Zisserman, A., Damen, D.: Epic-fusion: audio-visual temporal binding for egocentric action recognition. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00559"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Kazakos, E., Nagrani, A., Zisserman, A., Damen, D.: Slow-fast auditory streams for audio recognition. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 855\u2013859. IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9413376"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Kim, D., Tsai, Y.H., Zhuang, B., Yu, X., Sclaroff, S., Saenko, K., Chandraker, M.: Learning cross-modal contrastive features for video domain adaptation. In: ICCV, pp. 13,618\u201313,627 (2021)","DOI":"10.1109\/ICCV48922.2021.01336"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Li, H., Jialin\u00a0Pan, S., Wang, S., Kot, A.C.: Domain generalization with adversarial feature learning. In: CVPR, pp. 5400\u20135409 (2018)","DOI":"10.1109\/CVPR.2018.00566"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Li, Y., Tian, X., Gong, M., Liu, Y., Liu, T., Zhang, K., Tao, D.: Deep domain generalization via conditional invariant adversarial networks. In: ECCV, pp. 624\u2013639 (2018)","DOI":"10.1609\/aaai.v32i1.11682"},{"key":"16_CR35","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.patcog.2018.03.005","volume":"80","author":"Y Li","year":"2018","unstructured":"Li, Y., Wang, N., Shi, J., Hou, X., Liu, J.: Adaptive batch normalization for practical domain adaptation. Pattern Recogn. 80, 109\u2013117 (2018)","journal-title":"Pattern Recogn."},{"key":"16_CR36","unstructured":"Li, Y., Wang, N., Shi, J., Liu, J., Hou, X.: Revisiting batch normalization for practical domain adaptation. In: ICLR. OpenReview.net (2017). URL https:\/\/openreview.net\/forum?id=Hk6dkJQFx"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Lin, J., Gan, C., Han, S.: Tsm: Temporal shift module for efficient video understanding. In: ICCV, pp. 7083\u20137093 (2019)","DOI":"10.1109\/ICCV.2019.00718"},{"key":"16_CR38","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: ICML, pp. 97\u2013105. PMLR (2015)"},{"issue":"8","key":"16_CR39","doi-asserted-by":"publisher","first-page":"3703","DOI":"10.1109\/TIP.2019.2901707","volume":"28","author":"M Lu","year":"2019","unstructured":"Lu, M., Li, Z., Wang, Y., Pan, G.: Deep attention network for egocentric action recognition. IEEE Trans. Image Process. 28(8), 3703\u20133713 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"16_CR40","doi-asserted-by":"crossref","unstructured":"Lu, M., Liao, D., Li, Z.N.: Learning spatiotemporal attention for egocentric action recognition. In: ICCV Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00543"},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Ma, M., Fan, H., Kitani, K.M.: Going deeper into first-person activity recognition. In: CVPR, pp. 1894\u20131903 (2016)","DOI":"10.1109\/CVPR.2016.209"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Munro, J., Damen, D.: Multi-modal domain adaptation for fine-grained action recognition. In: CVPR, pp. 122\u2013132 (2020)","DOI":"10.1109\/CVPR42600.2020.00020"},{"key":"16_CR43","doi-asserted-by":"crossref","unstructured":"Pan, B., Cao, Z., Adeli, E., Niebles, J.C.: Adversarial cross-domain action recognition with co-attention. In: AAAI, pp. 11815\u201311822 (2020)","DOI":"10.1609\/aaai.v34i07.6854"},{"key":"16_CR44","unstructured":"Perez-Rua, J.M., Martinez, B., Zhu, X., Toisoul, A., Escorcia, V., Xiang, T.: Knowing what, where and when to look: efficient video action modeling with attention (2020)"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Planamente, M., Bottino, A., Caputo, B.: Self-supervised joint encoding of motion and appearance for first person action recognition. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 8751\u20138758. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9411972"},{"key":"16_CR46","doi-asserted-by":"crossref","unstructured":"Planamente, M., Plizzari, C., Alberti, E., Caputo, B.: Domain generalization through audio-visual relative norm alignment in first person action recognition. In: WACV, pp. 1807\u20131818 (2022)","DOI":"10.1109\/WACV51458.2022.00024"},{"key":"16_CR47","doi-asserted-by":"crossref","unstructured":"Plananamente, M., Plizzari, C., Caputo, B.: Test-time adaptation for egocentric action recognition. In: International Conference on Image Analysis and Processing, pp. 206\u2013218. Springer (2022)","DOI":"10.1007\/978-3-031-06433-3_18"},{"key":"16_CR48","unstructured":"Plizzari, C., Planamente, M., Alberti, E., Caputo, B.: Polito-iit submission to the epic-kitchens-100 unsupervised domain adaptation challenge for action recognition. arXiv preprint arXiv:2107.00337 (2021)"},{"key":"16_CR49","doi-asserted-by":"crossref","unstructured":"Plizzari, C., Planamente, M., Goletto, G., Cannici, M., Gusso, E., Matteucci, M., Caputo, B.: E$$\\hat{\\,}$$ 2 (go) motion: Motion augmented event stream for egocentric action recognition. arXiv preprint arXiv:2112.03596 (2021)","DOI":"10.1109\/CVPR52688.2022.01931"},{"key":"16_CR50","doi-asserted-by":"crossref","unstructured":"Rodin, I., Furnari, A., Mavroeidis, D., Farinella, G.M.: Predicting the future from first person (egocentric) vision: a survey. Comput. Vis. Image Understand. 211, 103252 (2021)","DOI":"10.1016\/j.cviu.2021.103252"},{"key":"16_CR51","doi-asserted-by":"crossref","unstructured":"Roy, S., Siarohin, A., Sangineto, E., Bulo, S.R., Sebe, N., Ricci, E.: Unsupervised domain adaptation using feature-whitening and consensus loss. In: CVPR, pp. 9471\u20139480 (2019)","DOI":"10.1109\/CVPR.2019.00970"},{"key":"16_CR52","unstructured":"Sahoo, A., Shah, R., Panda, R., Saenko, K., Das, A.: Contrast and mix: temporal contrastive video domain adaptation with background mixing. NeurIPS 34 (2021)"},{"key":"16_CR53","doi-asserted-by":"crossref","unstructured":"Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. In: CVPR, pp. 3723\u20133732 (2018)","DOI":"10.1109\/CVPR.2018.00392"},{"key":"16_CR54","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NeurIPS (NIPS\u201914), pp. 568\u2013576. MIT Press, Cambridge, MA, USA (2014)"},{"key":"16_CR55","doi-asserted-by":"crossref","unstructured":"Singh, S., Arora, C., Jawahar, C.: First person action recognition using deep learned descriptors. In: CVPR, pp. 2620\u20132628 (2016)","DOI":"10.1109\/CVPR.2016.287"},{"key":"16_CR56","doi-asserted-by":"crossref","unstructured":"Song, X., Zhao, S., Yang, J., Yue, H., Xu, P., Hu, R., Chai, H.: Spatio-temporal contrastive domain adaptation for action recognition. In: CVPR, pp. 9787\u20139795 (2021)","DOI":"10.1109\/CVPR46437.2021.00966"},{"key":"16_CR57","doi-asserted-by":"crossref","unstructured":"Sudhakaran, S., Escalera, S., Lanz, O.: Hierarchical feature aggregation networks for video action recognition. arXiv preprint arXiv:1905.12462 (2019)","DOI":"10.1109\/CVPR42600.2020.00118"},{"key":"16_CR58","doi-asserted-by":"crossref","unstructured":"Sudhakaran, S., Escalera, S., Lanz, O.: Lsta: Long short-term attention for egocentric action recognition. In: CVPR, pp. 9954\u20139963 (2019)","DOI":"10.1109\/CVPR.2019.01019"},{"key":"16_CR59","doi-asserted-by":"crossref","unstructured":"Sudhakaran, S., Lanz, O.: Convolutional long short-term memory networks for recognizing first person interactions. In: ICCV Workshops (2017)","DOI":"10.1109\/ICCVW.2017.276"},{"key":"16_CR60","doi-asserted-by":"crossref","unstructured":"Sudhakaran, S., Lanz, O.: Attention is all we need: nailing down object-centric attention for egocentric activity recognition. arXiv preprint arXiv:1807.11794 (2018)","DOI":"10.1109\/CVPR.2019.01019"},{"key":"16_CR61","doi-asserted-by":"crossref","unstructured":"Tang, H., Jia, K.: Discriminative adversarial domain adaptation. In: AAAI, pp. 5940\u20135947 (2020)","DOI":"10.1609\/aaai.v34i04.6054"},{"key":"16_CR62","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: ICCV, pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"16_CR63","unstructured":"Volpi, R., Namkoong, H., Sener, O., Duchi, J.C., Murino, V., Savarese, S.: Generalizing to unseen domains via adversarial data augmentation. In: NeurIPS, pp. 5334\u20135344 (2018)"},{"key":"16_CR64","doi-asserted-by":"crossref","unstructured":"Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., Van\u00a0Gool, L.: Temporal segment networks: Towards good practices for deep action recognition. In: ECCV, pp. 20\u201336. Springer, Berlin (2016)","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"16_CR65","unstructured":"Wang, X., Wu, Y., Zhu, L., Yang, Y., Zhuang, Y.: Symbiotic attention: Uts-baidu submission to the epic-kitchens 2020 action recognition challenge"},{"key":"16_CR66","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Feichtenhofer, C., Fan, H., He, K., Krahenbuhl, P., Girshick, R.: Long-term feature banks for detailed video understanding. In: CVPR, pp. 1426\u20131435 (2019)","DOI":"10.1109\/CVPR.2019.00037"},{"key":"16_CR67","doi-asserted-by":"crossref","unstructured":"Xu, R., Li, G., Yang, J., Lin, L.: Larger norm more transferable: an adaptive feature norm approach for unsupervised domain adaptation. In: ICCV, pp. 1426\u20131435 (2019)","DOI":"10.1109\/ICCV.2019.00151"},{"key":"16_CR68","unstructured":"Yang, L., Huang, Y., Sugano, Y., Sato, Y.: Epic-kitchens-100 unsupervised domain adaptation challenge for action recognition 2021: Team m3em technical report. arXiv preprint arXiv:2106.10026 (2021)"},{"key":"16_CR69","doi-asserted-by":"crossref","unstructured":"Zhou, B., Andonian, A., Oliva, A., Torralba, A.: Temporal relational reasoning in videos. In: ECCV, pp. 803\u2013818 (2018)","DOI":"10.1007\/978-3-030-01246-5_49"}],"container-title":["Springer Proceedings in Advanced Robotics","Human-Friendly Robotics 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22731-8_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T13:20:44Z","timestamp":1672579244000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22731-8_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031227301","9783031227318"],"references-count":69,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22731-8_16","relation":{},"ISSN":["2511-1256","2511-1264"],"issn-type":[{"type":"print","value":"2511-1256"},{"type":"electronic","value":"2511-1264"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"2 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HFR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Human-Friendly Robotics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Delft","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hfr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/hfr2022\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}