{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T11:18:03Z","timestamp":1780571883717,"version":"3.54.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004721","name":"The University of Tokyo","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004721","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Robot Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Human-robot interaction (HRI) is inherently challenging due to the dynamic and unpredictable nature of human motion, which complicates the determination of appropriate timing and location for interaction. To address this challenge, we propose a novel framework that integrates skeleton-based action recognition with motion prediction, enabling safe and adaptive trajectory generation. Action recognition is used to determine when to interact, while motion prediction is used to determine where to interact. Since human actions inherently influence subsequent motion patterns, the combination of these two components yields a more comprehensive understanding of human intent, thereby improving both prediction accuracy and interaction timing. A dual-level deep learning architecture is introduced to implement the proposed method. The high-level module performs human action recognition for interaction timing by capturing spatial features, whereas the low-level module focuses on human motion prediction by modeling temporal dynamics. The fusion of these features reduces uncertainty in future motion prediction. Based on the predicted human motion, the robot generates collision-free trajectories that dynamically adapt to human actions. The proposed method was evaluated against state-of-the-art methods and further validated through human-to-robot handover experiments. Experimental results demonstrate that the method achieves superior prediction accuracy, enables real-time trajectory adaptation, and enhances the adaptability of robots in interactive scenarios.<\/jats:p>","DOI":"10.1007\/s10846-025-02333-1","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T09:14:05Z","timestamp":1765876445000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Human-Robot Interaction with Skeleton-Based Action Recognition and Motion Prediction"],"prefix":"10.1007","volume":"111","author":[{"given":"Fan","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Nishio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Ota","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"2333_CR1","doi-asserted-by":"crossref","unstructured":"Kuffner, J.J., LaValle, S.M.: RRT-connect: An efficient approach to single-query path planning. In: Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), vol. 2, pp. 995\u20131001. IEEE (2000)","DOI":"10.1109\/ROBOT.2000.844730"},{"key":"2333_CR2","doi-asserted-by":"crossref","unstructured":"Ebert, D.M., Henrich, D.D.: Safe human-robot-cooperation: Image-based collision detection for industrial robots. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems, vol. 2, pp. 1826\u20131831. IEEE (2002)","DOI":"10.1109\/IRDS.2002.1044021"},{"key":"2333_CR3","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.conengprac.2018.07.004","volume":"79","author":"T Ren","year":"2018","unstructured":"Ren, T., Dong, Y., Wu, D., Chen, K.: Collision detection and identification for robot manipulators based on extended state observer. Control. Eng. Pract. 79, 144\u2013153 (2018)","journal-title":"Control. Eng. Pract."},{"issue":"9","key":"2333_CR4","doi-asserted-by":"publisher","first-page":"6687","DOI":"10.1007\/s00500-019-04306-7","volume":"24","author":"A-N Sharkawy","year":"2020","unstructured":"Sharkawy, A.-N., Koustoumpardis, P.N., Aspragathos, N.: Human-robot collisions detection for safe human-robot interaction using one multi-input-output neural network. Soft. Comput. 24(9), 6687\u20136719 (2020)","journal-title":"Soft. Comput."},{"key":"2333_CR5","doi-asserted-by":"publisher","first-page":"4389","DOI":"10.1007\/s12206-014-1006-5","volume":"28","author":"B-J Jung","year":"2014","unstructured":"Jung, B.-J., Koo, J.C., Choi, H.R., Moon, H.: Human-robot collision detection under modeling uncertainty using frequency boundary of manipulator dynamics. J. Mech. Sci. Technol. 28, 4389\u20134395 (2014)","journal-title":"J. Mech. Sci. Technol."},{"issue":"3","key":"2333_CR6","doi-asserted-by":"publisher","first-page":"6046","DOI":"10.1109\/LRA.2021.3086666","volume":"6","author":"L Vianello","year":"2021","unstructured":"Vianello, L., Mouret, J.-B., Dalin, E., Aubry, A., Ivaldi, S.: Human posture prediction during physical human-robot interaction. IEEE Robot. Autom. Lett. 6(3), 6046\u20136053 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"17","key":"2333_CR7","doi-asserted-by":"publisher","first-page":"5748","DOI":"10.3390\/s21175748","volume":"21","author":"S Grushko","year":"2021","unstructured":"Grushko, S., Vysock\u1ef3, A., Heczko, D., Bobovsk\u1ef3, Z.: Intuitive spatial tactile feedback for better awareness about robot trajectory during human-robot collaboration. Sensors 21(17), 5748 (2021)","journal-title":"Sensors"},{"key":"2333_CR8","doi-asserted-by":"crossref","unstructured":"Singi, S., He, Z., Pan, A., Patel, S., Sigurdsson, G.A., Piramuthu, R., Song, S., Ciocarlie, M.: Decision making for human-in-the-loop robotic agents via uncertainty-aware reinforcement learning. In: 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 7939\u20137945. IEEE (2024)","DOI":"10.1109\/ICRA57147.2024.10611425"},{"issue":"3","key":"2333_CR9","doi-asserted-by":"publisher","first-page":"2300359","DOI":"10.1002\/aisy.202300359","volume":"6","author":"T Wang","year":"2024","unstructured":"Wang, T., Zheng, P., Li, S., Wang, L.: Multimodal human-robot interaction for human-centric smart manufacturing: A survey. Adv. Intell. Syst. 6(3), 2300359 (2024)","journal-title":"Adv. Intell. Syst."},{"issue":"1","key":"2333_CR10","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1007\/s40747-023-01173-6","volume":"10","author":"J Qi","year":"2024","unstructured":"Qi, J., Ma, L., Cui, Z., Yu, Y.: Computer vision-based hand gesture recognition for human-robot interaction: A review. Complex Intell. Syst. 10(1), 1581\u20131606 (2024)","journal-title":"Complex Intell. Syst."},{"key":"2333_CR11","doi-asserted-by":"crossref","unstructured":"Obrenovic, B., Gu, X., Wang, G., Godinic, D., Jakhongirov, I.: Generative AI and human\u2013robot interaction: Implications and future agenda for business, society and ethics. AI & society, 1\u201314 (2024)","DOI":"10.1007\/s00146-024-01889-0"},{"issue":"4","key":"2333_CR12","doi-asserted-by":"publisher","first-page":"3854","DOI":"10.1109\/TIE.2021.3075852","volume":"69","author":"Z Wu","year":"2021","unstructured":"Wu, Z., Chen, Y., Liang, J., He, B., Wang, Y.: ST-FMT*: A fast optimal global motion planning for mobile robot. IEEE Trans. Industr. Electron. 69(4), 3854\u20133864 (2021)","journal-title":"IEEE Trans. Industr. Electron."},{"issue":"8","key":"2333_CR13","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1007\/s12555-019-0076-7","volume":"18","author":"S-O Park","year":"2020","unstructured":"Park, S.-O., Lee, M.C., Kim, J.: Trajectory planning with collision avoidance for redundant robots using Jacobian and artificial potential field-based real-time inverse kinematics. Int. J. Control Autom. Syst. 18(8), 2095\u20132107 (2020)","journal-title":"Int. J. Control Autom. Syst."},{"key":"2333_CR14","doi-asserted-by":"publisher","first-page":"103705","DOI":"10.1016\/j.robot.2020.103705","volume":"136","author":"H Niu","year":"2021","unstructured":"Niu, H., Ma, C., Han, P.: Directional optimal reciprocal collision avoidance. Robot. Auton. Syst. 136, 103705 (2021)","journal-title":"Robot. Auton. Syst."},{"issue":"1","key":"2333_CR15","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1109\/TSMC.2018.2833384","volume":"49","author":"J Quintas","year":"2018","unstructured":"Quintas, J., Martins, G.S., Santos, L., Menezes, P., Dias, J.: Toward a context-aware human-robot interaction framework based on cognitive development. IEEE Trans. Syst. Man Cybern.: Syst. 49(1), 227\u2013237 (2018)","journal-title":"IEEE Trans. Syst. Man Cybern.: Syst."},{"key":"2333_CR16","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s00170-018-2788-x","volume":"101","author":"P Neto","year":"2019","unstructured":"Neto, P., Sim\u00e3o, M., Mendes, N., Safeea, M.: Gesture-based human-robot interaction for human assistance in manufacturing. Int. J. Adv. Manuf. Technol. 101, 119\u2013135 (2019)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"2333_CR17","doi-asserted-by":"crossref","unstructured":"Kuo, P.-H., Shen, Y.-C., Feng, P.-H., Chiu, Y.-J., Yau, H.-T.: Transfer learning-based gesture and pose recognition system for human robot interaction: An internet of things application. IEEE Internet of Things Journal, 1\u20131 (2024)","DOI":"10.1109\/JIOT.2024.3436584"},{"key":"2333_CR18","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, S., Zhao, Y., Zhang, Y., Wang, Y., Tian, Q.: Dynamic multiscale graph neural networks for 3D skeleton based human motion prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 214\u2013223 (2020)","DOI":"10.1109\/CVPR42600.2020.00029"},{"key":"2333_CR19","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Gonz\u00e1lez, A., Villamizar, M., Odobez, J.-M.: Pose Transformers (POTR): Human motion prediction with non-autoregressive transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2276\u20132284 (2021)","DOI":"10.1109\/ICCVW54120.2021.00257"},{"key":"2333_CR20","doi-asserted-by":"crossref","unstructured":"Sidiropoulos, A., Karayiannidis, Y., Doulgeri, Z.: Human-robot collaborative object transfer using human motion prediction based on cartesian pose dynamic movement primitives. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 3758\u20133764. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9562035"},{"key":"2333_CR21","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/s10514-016-9556-2","volume":"41","author":"GJ Maeda","year":"2017","unstructured":"Maeda, G.J., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., Peters, J.: Probabilistic movement primitives for coordination of multiple human-robot collaborative tasks. Auton. Robot. 41, 593\u2013612 (2017)","journal-title":"Auton. Robot."},{"key":"2333_CR22","doi-asserted-by":"crossref","unstructured":"Mainprice, J., Berenson, D.: Human-robot collaborative manipulation planning using early prediction of human motion. In: 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 299\u2013306. IEEE (2013)","DOI":"10.1109\/IROS.2013.6696368"},{"key":"2333_CR23","doi-asserted-by":"crossref","unstructured":"Fragkiadaki, K., Levine, S., Felsen, P., Malik, J.: Recurrent network models for human dynamics. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4346\u20134354 (2015)","DOI":"10.1109\/ICCV.2015.494"},{"key":"2333_CR24","doi-asserted-by":"crossref","unstructured":"Martinez, J., Black, M.J., Romero, J.: On human motion prediction using recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2891\u20132900 (2017)","DOI":"10.1109\/CVPR.2017.497"},{"key":"2333_CR25","doi-asserted-by":"crossref","unstructured":"Jain, A., Zamir, A.R., Savarese, S., Saxena, A.: Structural-RNN: Deep learning on spatio-temporal graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5308\u20135317 (2016)","DOI":"10.1109\/CVPR.2016.573"},{"key":"2333_CR26","doi-asserted-by":"crossref","unstructured":"Ghosh, P., Song, J., Aksan, E., Hilliges, O.: Learning human motion models for long-term predictions. In: 2017 International Conference on 3D Vision (3DV), pp. 458\u2013466. IEEE (2017)","DOI":"10.1109\/3DV.2017.00059"},{"key":"2333_CR27","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.neucom.2021.10.011","volume":"468","author":"J Tang","year":"2022","unstructured":"Tang, J., Zhang, J., Yin, J.: Temporal consistency two-stream CNN for human motion prediction. Neurocomputing 468, 245\u2013256 (2022)","journal-title":"Neurocomputing"},{"key":"2333_CR28","doi-asserted-by":"crossref","unstructured":"Li, C., Zhang, Z., Lee, W.S., Lee, G.H.: Convolutional sequence to sequence model for human dynamics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5226\u20135234 (2018)","DOI":"10.1109\/CVPR.2018.00548"},{"key":"2333_CR29","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1007\/s00371-019-01692-9","volume":"35","author":"Y Li","year":"2019","unstructured":"Li, Y., Wang, Z., Yang, X., Wang, M., Poiana, S.I., Chaudhry, E., Zhang, J.: Efficient convolutional hierarchical autoencoder for human motion prediction. Vis. Comput. 35, 1143\u20131156 (2019)","journal-title":"Vis. Comput."},{"key":"2333_CR30","doi-asserted-by":"crossref","unstructured":"Butepage, J., Black, M.J., Kragic, D., Kjellstrom, H.: Deep representation learning for human motion prediction and classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6158\u20136166 (2017)","DOI":"10.1109\/CVPR.2017.173"},{"key":"2333_CR31","doi-asserted-by":"crossref","unstructured":"Cui, Q., Sun, H., Yang, F.: Learning dynamic relationships for 3D human motion prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6519\u20136527 (2020)","DOI":"10.1109\/CVPR42600.2020.00655"},{"key":"2333_CR32","doi-asserted-by":"crossref","unstructured":"Mao, W., Liu, M., Salzmann, M., Li, H.: Learning trajectory dependencies for human motion prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9489\u20139497 (2019)","DOI":"10.1109\/ICCV.2019.00958"},{"key":"2333_CR33","doi-asserted-by":"crossref","unstructured":"Liu, C., Chen, Y., Liu, M., Shi, B.E.: AVGCN: Trajectory prediction using graph convolutional networks guided by human attention. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 14234\u201314240. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9560908"},{"key":"2333_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhao, S., Meng, F., Wu, S., Liu, M.: Dynamic compositional graph convolutional network for efficient composite human motion prediction. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 2856\u20132864 (2023)","DOI":"10.1145\/3581783.3612532"},{"key":"2333_CR35","doi-asserted-by":"crossref","unstructured":"Aksan, E., Kaufmann, M., Cao, P., Hilliges, O.: A spatio-temporal transformer for 3D human motion prediction. In: 2021 International Conference on 3D Vision (3DV), pp. 565\u2013574. IEEE (2021)","DOI":"10.1109\/3DV53792.2021.00066"},{"key":"2333_CR36","doi-asserted-by":"crossref","unstructured":"Aksan, E., Cao, P., Kaufmann, M., Hilliges, O.: Attention, please: A spatio-temporal transformer for 3D human motion prediction. 2(3), 5 (2020). arXiv:2004.08692","DOI":"10.1109\/3DV53792.2021.00066"},{"key":"2333_CR37","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2022.08.075","volume":"511","author":"C Zhong","year":"2022","unstructured":"Zhong, C., Hu, L., Xia, S.: Spatial-temporal modeling for prediction of stylized human motion. Neurocomputing 511, 34\u201342 (2022)","journal-title":"Neurocomputing"},{"key":"2333_CR38","unstructured":"Nargund, A.A., Sra, M.: SPOTR: Spatio-temporal pose transformers for human motion prediction (2023). arXiv:2303.06277"},{"key":"2333_CR39","doi-asserted-by":"crossref","unstructured":"Medjaouri, O., Desai, K.: HR-STAN: High-resolution spatio-temporal attention network for 3D human motion prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2540\u20132549 (2022)","DOI":"10.1109\/CVPRW56347.2022.00286"},{"key":"2333_CR40","doi-asserted-by":"crossref","unstructured":"Xin, W., Liu, R., Liu, Y., Chen, Y., Yu, W., Miao, Q.: Transformer for skeleton-based action recognition: A review of recent advances. Neurocomputing (2023)","DOI":"10.1016\/j.neucom.2023.03.001"},{"key":"2333_CR41","doi-asserted-by":"crossref","unstructured":"Xu, C., Tan, R.T., Tan, Y., Chen, S., Wang, X., Wang, Y.: Auxiliary tasks benefit 3D skeleton-based human motion prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9509\u20139520 (2023)","DOI":"10.1109\/ICCV51070.2023.00872"},{"key":"2333_CR42","doi-asserted-by":"crossref","unstructured":"Cui, Q., Sun, H.: Towards accurate 3D human motion prediction from incomplete observations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4801\u20134810 (2021)","DOI":"10.1109\/CVPR46437.2021.00477"},{"key":"2333_CR43","doi-asserted-by":"crossref","unstructured":"Li, S., Liu, Z.-Q., Chan, A.B.: Heterogeneous multi-task learning for human pose estimation with deep convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 482\u2013489 (2014)","DOI":"10.1109\/CVPRW.2014.78"},{"issue":"7","key":"2333_CR44","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2013","unstructured":"Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3. 6m: Large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325\u20131339 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2333_CR45","doi-asserted-by":"crossref","unstructured":"Dang, L., Nie, Y., Long, C., Zhang, Q., Li, G.: MSR-GCN: Multi-scale residual graph convolution networks for human motion prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11467\u201311476 (2021)","DOI":"10.1109\/ICCV48922.2021.01127"},{"key":"2333_CR46","doi-asserted-by":"crossref","unstructured":"Ma, T., Nie, Y., Long, C., Zhang, Q., Li, G.: Progressively generating better initial guesses towards next stages for high-quality human motion prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6437\u20136446 (2022)","DOI":"10.1109\/CVPR52688.2022.00633"},{"key":"2333_CR47","doi-asserted-by":"crossref","unstructured":"Guo, W., Du, Y., Shen, X., Lepetit, V., Alameda-Pineda, X., Moreno-Noguer, F.: Back to MLP: A simple baseline for human motion prediction. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 4809\u20134819 (2023)","DOI":"10.1109\/WACV56688.2023.00479"},{"key":"2333_CR48","doi-asserted-by":"crossref","unstructured":"Xu, C., Tan, R.T., Tan, Y., Chen, S., Wang, Y.G., Wang, X., Wang, Y.: Eqmotion: Equivariant multi-agent motion prediction with invariant interaction reasoning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1410\u20131420 (2023)","DOI":"10.1109\/CVPR52729.2023.00142"},{"key":"2333_CR49","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"2333_CR50","doi-asserted-by":"crossref","unstructured":"Li, C., Zhong, Q., Xie, D., Pu, S.: Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 786\u2013792 (2018)","DOI":"10.24963\/ijcai.2018\/109"},{"key":"2333_CR51","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12026\u201312035 (2019)","DOI":"10.1109\/CVPR.2019.01230"},{"issue":"6","key":"2333_CR52","doi-asserted-by":"publisher","first-page":"3316","DOI":"10.1109\/TPAMI.2021.3053765","volume":"44","author":"M Li","year":"2022","unstructured":"Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., Tian, Q.: Symbiotic graph neural networks for 3D skeleton-based human action recognition and motion prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3316\u20133333 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2333_CR53","unstructured":"Keskar, N.S., Socher, R.: Improving generalization performance by switching from Adam to SGD (2017). arXiv:1712.07628"},{"key":"2333_CR54","doi-asserted-by":"crossref","unstructured":"Yang, W., Paxton, C., Mousavian, A., Chao, Y.-W., Cakmak, M., Fox, D.: Reactive human-to-robot handovers of arbitrary objects. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 3118\u20133124 (2021)","DOI":"10.1109\/ICRA48506.2021.9561170"}],"container-title":["Journal of Intelligent &amp; Robotic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10846-025-02333-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10846-025-02333-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10846-025-02333-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T07:49:16Z","timestamp":1768031356000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10846-025-02333-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,16]]},"references-count":54,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2333"],"URL":"https:\/\/doi.org\/10.1007\/s10846-025-02333-1","relation":{},"ISSN":["1573-0409"],"issn-type":[{"value":"1573-0409","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,16]]},"assertion":[{"value":"21 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"129"}}