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The study integrates an adaptive HRI hand for versatile grasping and incorporates image recognition for efficient object identification and precise coordinate estimation. A tailored reinforcement-learning environment enables the robot to dynamically adapt to diverse scenarios. The effectiveness of this approach is validated through simulations and real-world applications. The HRI hand\u2019s adaptability ensures seamless interactions, while image recognition enhances cognitive capabilities. The reinforcement-learning framework enables the robot to learn and refine skills, demonstrated through successful navigation and manipulation in various scenarios. The transition from simulations to real-world applications affirms the practicality of the proposed system, showcasing its robustness and potential for integration into practical robotic platforms. This study contributes to advancing intelligent and adaptable robotic systems for safe and dynamic HRIs.<\/jats:p>","DOI":"10.3390\/s24196275","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T11:19:33Z","timestamp":1727435973000},"page":"6275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Human-to-Robot Handover Based on Reinforcement Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3857-4291","authenticated-orcid":false,"given":"Myunghyun","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering (Age Service-Tech), Kyung Hee University, Seoul 02447, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3393-0269","authenticated-orcid":false,"given":"Sungwoo","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering (Age Service-Tech), Kyung Hee University, Seoul 02447, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1856-2495","authenticated-orcid":false,"given":"Beomjoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, College of Software, Kyung Hee University, Seoul 02447, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2349-0801","authenticated-orcid":false,"given":"Jinyeob","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, College of Software, Kyung Hee University, Seoul 02447, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0477-3847","authenticated-orcid":false,"given":"Donghan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering (Age Service-Tech), Kyung Hee University, Seoul 02447, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"99467","DOI":"10.1109\/ACCESS.2021.3094060","article-title":"Design and experiment of an anthropomorphic robot hand for variable grasping stiffness","volume":"9","author":"Park","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Scherzinger, S., Roennau, A., and Dillmann, R. (2019, January 2\u20136). Inverse kinematics with forward dynamics solvers for sampled motion tracking. Proceedings of the 19th International Conference on Advanced Robotics (ICAR), IEEE, Belo Horizonte, Brazil.","DOI":"10.1109\/ICAR46387.2019.8981554"},{"key":"ref_3","first-page":"729","article-title":"Reinforcement learning","volume":"12","author":"Wiering","year":"2012","journal-title":"Adapt. Learn. Optim."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Curioni, A., Knoblich, G., Sebanz, N., Goswami, A., and Vadakkepat, P. (2019). Joint action in humans: A model for human-robot interactions. Humanoid Robotics: A Reference, Springer.","DOI":"10.1007\/978-94-007-6046-2_126"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102231","DOI":"10.1016\/j.rcim.2021.102231","article-title":"A survey of robot learning strategies for human-robot collaboration in industrial settings","volume":"73","author":"Mukherjee","year":"2022","journal-title":"Robot. Comput. -Integr. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Castro, A., Filipe, S., and Vitor, S. (2021). Trends of human-robot collaboration in industry contexts: Handover, learning, and metrics. Sensors, 21.","DOI":"10.3390\/s21124113"},{"key":"ref_7","unstructured":"Miller, A.T., Knoop, S., Christensen, H.I., and Allen, P.K. (2003, January 14\u201319). Automatic grasp planning using shape primitives. Proceedings of the IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422), Taipei, Taiwan."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lundell, J., Francesco, V., and Ville, K. (2019, January 3\u20138). Robust grasp planning over uncertain shape completions. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967816"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MRA.2020.2976322","article-title":"Multifingered grasp planning via inference in deep neural networks: Outperforming sampling by learning differentiable models","volume":"27","author":"Lu","year":"2020","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_10","unstructured":"Yang, W., Paxton, C., Mousavian, A., Chao, Y.W., Cakmak, M., and Fox, D. (June, January 30). Reactive human-to-robot handovers of arbitrary objects. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE, Xi\u2019an, China."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1109\/TRO.2021.3075365","article-title":"Object handovers: A review for robotics","volume":"37","author":"Ortenzi","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yue, X., Li, H., Shimizu, M., Kawamura, S., and Meng, L. (2022). YOLO-GD: A deep learning-based object detection algorithm for empty-dish recycling robots. Machines, 10.","DOI":"10.3390\/machines10050294"},{"key":"ref_13","unstructured":"(2024, August 11). Object Tracker. Available online: https:\/\/github.com\/QualiaT\/object_tracker."},{"key":"ref_14","unstructured":"Taunyazov, T., Song, L.S., Lim, E., See, H.H., Lee, D., Tee, B.C., and Soh, H. (October, January 27). Extended tactile perception: Vibration sensing through tools and grasped objects. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Prague, Czech Republic."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pang, Y.L., Xompero, A., Oh, C., and Cavallaro, A. (2021, January 8\u201312). Towards safe human-to-robot handovers of unknown containers. Proceedings of the 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), Vancouver, BC, Canada.","DOI":"10.1109\/RO-MAN50785.2021.9515350"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Christen, S., Yang, W., P\u00e9rez-D\u2019Arpino, C., Hilliges, O., Fox, D., and Chao, Y.W. (2023, January 17\u201324). Learning human-to-robot handovers from point clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00931"},{"key":"ref_17","unstructured":"Wang, L., Xiang, Y., Yang, W., Mousavian, A., and Fox, D. (2022, January 14\u201318). Goal-auxiliary actor-critic for 6d robotic grasping with point clouds. Proceedings of the Conference on Robot Learning, PMLR, Auckland, New Zealand."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gupta, A., Eppner, C., Levine, S., and Abbeel, P. (2016, January 9\u201314). Learning dexterous manipulation for a soft robotic hand from human demonstrations. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE,  Daejeon, Republic of Korea.","DOI":"10.1109\/IROS.2016.7759557"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nguyen, H., and Hung, L.A. (2019, January 25\u201327). Review of deep reinforcement learning for robot manipulation. Proceedings of the Third IEEE International Conference on Robotic Computing (IRC), IEEE, Naples, Italy.","DOI":"10.1109\/IRC.2019.00120"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3933","DOI":"10.1109\/LRA.2021.3067299","article-title":"Evaluating guided policy search for human-robot handovers","volume":"6","author":"Kshirsagar","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_21","unstructured":"Chang, P.-K., Huang, J.T., Huang, Y.Y., and Wang, H.C. (2022, January 23\u201327). Learning end-to-end 6dof grasp choice of human-to-robot handover using affordance prediction and deep reinforcement learning. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, W., Sundaralingam, B., Paxton, C., Akinola, I., Chao, Y.W., Cakmak, M., and Fox, D. (2022, January 23\u201327). Model predictive control for fluid human-to-robot handovers. Proceedings of the 2022 International Conference on Robotics and Automation, ICRA, Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9812109"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kedia, K., Bhardwaj, A., Dan, P., and Choudhury, S. (2024, January 13\u201317). InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10610681"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Duan, H., Li, Y., Li, D., Wei, W., Huang, Y., and Wang, P. (2024, January 13\u201317). Learning Realistic and Reasonable Grasps for Anthropomorphic Hand in Cluttered Scenes. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10610646"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Christen, S., Feng, L., Yang, W., Chao, Y.W., Hilliges, O., and Song, J. (2024, January 13\u201317). SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10610694"},{"key":"ref_26","unstructured":"(2024, August 11). Gazebo. Available online: https:\/\/gazebosim.org."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lucchi, M., Zindler, F., M\u00fchlbacher-Karrer, S., and Pichler, H. (2020\u201324, January 24). Robo-gym\u2013an open source toolkit for distributed deep reinforcement learning on real and simulated robots. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9340956"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6275\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:05:30Z","timestamp":1760112330000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,27]]},"references-count":27,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196275"],"URL":"https:\/\/doi.org\/10.3390\/s24196275","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,27]]}}}