{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T19:48:22Z","timestamp":1776196102792,"version":"3.50.1"},"reference-count":54,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"104","content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Robot."],"published-print":{"date-parts":[[2025,7,16]]},"abstract":"<jats:p>Surgical robots capable of autonomously performing various tasks could enhance efficiency and augment human productivity in addressing clinical needs. Although current solutions have automated specific actions within defined contexts, they are challenging to generalize across diverse environments in general surgery. Embodied intelligence enables general-purpose robot learning with applications for daily tasks, yet its application in the medical domain remains limited. We introduced an open-source surgical embodied intelligence simulator for an interactive environment to develop reinforcement learning methods for minimally invasive surgical robots. Using such embodied artificial intelligence, this study further addresses surgical task automation, enabling zero-shot transfer of simulation-trained policies to real-world scenarios. The proposed method encompasses visual parsing, a perceptual regressor, policy learning, and a visual servoing controller, forming a paradigm that combines the advantages of data-driven policy and classic controller. The visual parsing uses stereo depth estimation and image segmentation with a visual foundation model to handle complex scenes. Experiments demonstrated autonomy in seven game-based skill training tasks on the da Vinci Research Kit, with a proof-of-concept study on haptic-assisted skill training as a practical application. Moreover, we conducted automation of five surgical assistive tasks with the Sentire surgical system on ex vivo animal tissues with various scenes, object sizes, instrument types, and illuminations. The learned policies were also validated in a live-animal trial for three tasks in dynamic in vivo surgical environments. We hope this open-source infrastructure, coupled with a general-purpose learning paradigm, will inspire and facilitate future research on embodied intelligence toward autonomous surgical robots.<\/jats:p>","DOI":"10.1126\/scirobotics.adt3093","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T18:00:03Z","timestamp":1752688803000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":17,"title":["Surgical embodied intelligence for generalized task autonomy in laparoscopic robot-assisted surgery"],"prefix":"10.1126","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4474-7854","authenticated-orcid":true,"given":"Yonghao","family":"Long","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2550-598X","authenticated-orcid":true,"given":"Anran","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"given":"Derek Hang Chun","family":"Kwok","sequence":"additional","affiliation":[{"name":"Cornerstone Robotics Ltd., HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0353-4465","authenticated-orcid":true,"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cornerstone Robotics Ltd., HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3869-9011","authenticated-orcid":true,"given":"Zhenya","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9633-1792","authenticated-orcid":true,"given":"Kejian","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6096-4641","authenticated-orcid":true,"given":"Lei","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3964-5992","authenticated-orcid":true,"given":"Jiawei","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4280-7622","authenticated-orcid":true,"given":"Hongbin","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"given":"Wang","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1044-2456","authenticated-orcid":true,"given":"Kai","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7677-2600","authenticated-orcid":true,"given":"Xiangyu","family":"Chu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"given":"Yang","family":"Hu","sequence":"additional","affiliation":[{"name":"Cornerstone Robotics Ltd., HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1041-9668","authenticated-orcid":true,"given":"Hon Chi","family":"Yip","sequence":"additional","affiliation":[{"name":"Department of Surgery, Chinese University of Hong Kong, HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9292-112X","authenticated-orcid":true,"given":"Philip Wai Yan","family":"Chiu","sequence":"additional","affiliation":[{"name":"Department of Surgery, Chinese University of Hong Kong, HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6117-5467","authenticated-orcid":true,"given":"Peter","family":"Kazanzides","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johns Hopkins University, Baltimore, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6272-1100","authenticated-orcid":true,"given":"Russell H.","family":"Taylor","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johns Hopkins University, Baltimore, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3625-6679","authenticated-orcid":true,"given":"Yunhui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"given":"Zihan","family":"Chen","sequence":"additional","affiliation":[{"name":"Cornerstone Robotics Ltd., HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4281-5120","authenticated-orcid":true,"given":"Zerui","family":"Wang","sequence":"additional","affiliation":[{"name":"Cornerstone Robotics Ltd., HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0114-7499","authenticated-orcid":true,"family":"Samuel Kwok Wai Au","sequence":"additional","affiliation":[{"name":"Cornerstone Robotics Ltd., HKSAR, China."},{"name":"Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, HKSAR, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3416-9950","authenticated-orcid":true,"given":"Qi","family":"Dou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chinese University of Hong Kong, HKSAR, China."}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abi8017"},{"key":"e_1_3_2_3_2","unstructured":"Intuitive Surgical Inc. Annual report 2024 (2024); https:\/\/isrg.intuitive.com\/static-files\/500ff989-ad91-4b32-a59e-f94a34d75997."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2022.3176828"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abj2908"},{"key":"e_1_3_2_6_2","unstructured":"J. Gu F. Xiang X. Li Z. Ling X. Liu T. Mu Y. Tang S. Tao X. Wei Y. Yao X. Yuan P. Xie Z. Huang R. Chen H. Su \u201cManiSkill2: A unified benchmark for generalizable manipulation skills \u201d in Proceedings of the International Conference on Learning Representations (ICLR) (ICLR 2023) pp. 1\u201330."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07382-4"},{"key":"e_1_3_2_8_2","unstructured":"V. Makoviychuk L. Wawrzyniak Y. Guo M. Lu K. Storey M. Macklin D. Hoeller N. Rudin A. Allshire A. Handa G. State \u201cIsaac Gym: High performance GPU-based physics simulation for robot learning \u201d in Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS) (NeurIPS 2021) pp. 1\u201312."},{"key":"e_1_3_2_9_2","unstructured":"A. Szot A. Clegg E. Undersander E. Wijmans Y. Zhao J. Turner N. Maestre M. Mukadam D. Chaplot O. Maksymets A. Gokaslan V. Vondrus S. Dharur F. Meier W. Galuba A. Chang Z. Kira V. Koltun J. Malik M. Savva D. Batra \u201cHabitat 2.0: Training home assistants to rearrange their habitat \u201d in Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) (NeurIPS 2021) pp. 251\u2013266."},{"key":"e_1_3_2_10_2","unstructured":"F. Richter R. K. Orosco M. C. Yip Open-sourced reinforcement learning environments for surgical robotics. arXiv:1903.02090 [cs.RO] (2019)."},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"E. Tagliabue A. Pore D. Dall\u2019Alba E. Magnabosco M. Piccinelli P. Fiorini \u201cSoft tissue simulation environment to learn manipulation tasks in autonomous robotic surgery \u201d in Proceedings of the International Conference on Intelligent Robots and Systems (IROS) (IEEE 2020) pp. 3261\u20133266.","DOI":"10.1109\/IROS45743.2020.9341710"},{"key":"e_1_3_2_12_2","first-page":"1","article-title":"LapGym - An open source framework for reinforcement learning in robot-assisted laparoscopic surgery","volume":"24","author":"Scheikl P. M.","year":"2023","unstructured":"P. M. Scheikl, B. Gyenes, R. Younis, C. Haas, G. Neumann, M. Wagner, F. Mathis-Ullrich, LapGym - An open source framework for reinforcement learning in robot-assisted laparoscopic surgery. J. Mach. Learn. Res. 24, 1\u201342 (2023).","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","unstructured":"V. M. Varier D. K. Rajamani F. Tavakkolmoghaddam A. Munawar G. S. Fischer \u201cAMBF-RL: A real-time simulation based reinforcement learning toolkit for medical robotics \u201d in Proceedings of the International Symposium on Medical Robotics (ISMR) (IEEE 2022) pp. 1\u20138.","DOI":"10.1109\/ISMR48347.2022.9807609"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","unstructured":"S. Schmidgall A. Krieger J. Eshraghian \u201cSurgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots \u201d in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2024) pp. 13354\u201313361.","DOI":"10.1109\/ICRA57147.2024.10610448"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Q. Yu M. Moghani K. Dharmarajan V. Schorp W. C. H. Panitch J. Liu K. Hari H. Huang M. Mittal K. Goldberg A. Garg \u201cORBIT-Surgical: An open-simulation framework for learning surgical augmented dexterity \u201d in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2024) pp. 15509\u201315516.","DOI":"10.1109\/ICRA57147.2024.10611637"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"J. Xu B. Li B. Lu Y. H. Liu Q. Dou P. A. Heng \u201cSurRoL: An open-source reinforcement learning centered and dVRK compatible platform for surgical robot learning \u201d in Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2021) pp. 1821\u20131828.","DOI":"10.1109\/IROS51168.2021.9635867"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1142\/S2424905X23400044"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3266720"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3285478"},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","unstructured":"H. Zheng Z. J. Hu Y. Huang X. Cheng Z. Wang E. Burdet \u201cA user-centered shared control scheme with learning from demonstration for robotic surgery \u201d in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2024) pp. 15195\u201315201.","DOI":"10.1109\/ICRA57147.2024.10611089"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-014-3914-y"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","unstructured":"A. Nair B. McGrew M. Andrychowicz W. Zaremba P. Abbeel \u201cOvercoming exploration in reinforcement learning with demonstrations \u201d in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2018) pp. 6292\u20136299.","DOI":"10.1109\/ICRA.2018.8463162"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"M. Bain C. Sammut \u201cA framework for behavioural cloning\u201d in Machine Intelligence 15 Intelligent Agents (Oxford Univ. 2000) pp. 103\u2013129.","DOI":"10.1093\/oso\/9780198538677.003.0006"},{"key":"e_1_3_2_24_2","unstructured":"L. Chen K. Lu A. Rajeswaran K. Lee A. Grover M. Laskin P. Abbeel A. Srinivas I. Mordatch \u201cDecision transformer: Reinforcement learning via sequence modeling \u201d in Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) (NeurIPS 2021) pp. 15084\u201315097."},{"key":"e_1_3_2_25_2","unstructured":"V. G. Goecks G. M. Gremillion V. J. Lawhern J. Valasek N. R. Waytowich \u201cIntegrating behavior cloning and reinforcement learning for improved performance in dense and sparse reward environments \u201d in Proceedings of the International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2020) pp. 465\u2013473."},{"key":"e_1_3_2_26_2","unstructured":"A. Nair A. Gupta M. Dalal S. Levine AWAC: Accelerating online reinforcement learning with offline datasets. arXiv:2006.09359 [cs.LG] (2020)."},{"key":"e_1_3_2_27_2","unstructured":"I. Kostrikov A. Nair S. Levine \u201cOffline reinforcement learning with implicit Q-learning \u201d in Proceedings of the International Conference on Learning Representations (ICLR) (ICLR 2022) pp. 1\u201311."},{"key":"e_1_3_2_28_2","unstructured":"N. M. Shafiullah Z. Cui A. A. Altanzaya L. Pinto \u201cBehavior transformers: Cloning k modes with one stone \u201d in Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) (NeurIPS 2022) pp. 22955\u201322968."},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","unstructured":"T. Huang K. Chen B. Li Y. H. Liu Q. Dou \u201cDemonstration-guided reinforcement learning with efficient exploration for task automation of surgical robot \u201d in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2023) pp. 4640\u20134647.","DOI":"10.1109\/ICRA48891.2023.10160327"},{"key":"e_1_3_2_30_2","unstructured":"P. J. Ball L. Smith I. Kostrikov S. Levine \u201cEfficient online reinforcement learning with offline data \u201d in Proceedings of the International Conference on Machine Learning (ICML) (ICML 2023) pp. 1577\u20131594."},{"key":"e_1_3_2_31_2","unstructured":"J. Luo P. Dong Y. Zhai Y. Ma S. Levine \u201cRLIF: Interactive imitation learning as reinforcement learning \u201d in Proceedings of the International Conference on Learning Representations (ICLR) (ICLR 2024) pp. 1\u201323."},{"key":"e_1_3_2_32_2","unstructured":"Z. Fu T. Z. Zhao C. Finn \u201cMobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation \u201d in Proceedings of the Annual Conference on Robot Learning (CoRL) (CoRL 2024) pp. 1\u201318."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1177\/02783649241273668"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3284380"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-016-1425-0"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/0010-4655(94)00170-7"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201293"},{"key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3355089.3356506","article-title":"Taichi: A language for high-performance computation on spatially sparse data structures","volume":"38","author":"Hu Y.","year":"2019","unstructured":"Y. Hu, T.-M. Li, L. Anderson, J. Ragan-Kelley, F. Durand, Taichi: A language for high-performance computation on spatially sparse data structures. ACM Trans. Graph. 38, 1\u201316 (2019).","journal-title":"ACM Trans. Graph."},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3592433"},{"key":"e_1_3_2_40_2","unstructured":"Z. Yang K. Chen Y. Long Q. Dou SimEndoGS: Efficient data-driven scene simulation using robotic surgery videos via physics-embedded 3D Gaussians. arXiv:2405.00956 [cs.RO] (2024)."},{"key":"e_1_3_2_41_2","doi-asserted-by":"crossref","unstructured":"T. Osa C. Staub A. Knoll \u201cFramework of automatic robot surgery system using visual servoing \u201d in Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2010) pp. 1837\u20131842.","DOI":"10.1109\/IROS.2010.5650301"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1089\/lap.2013.0304"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3013914"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.adj3312"},{"key":"e_1_3_2_45_2","unstructured":"X. Zhao W. Ding Y. An Y. Du T. Yu M. Li M. Tang J. Wang Fast segment anything. arXiv:2306.12156 [cs.CV] (2023)."},{"key":"e_1_3_2_46_2","doi-asserted-by":"crossref","unstructured":"P. Kazanzides Z. Chen A. Deguet G. S. Fischer R. H. Taylor S. P. DiMaio \u201cAn open-source research kit for the da Vinci surgical system \u201d in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2014) pp. 5434\u20136439.","DOI":"10.1109\/ICRA.2014.6907809"},{"key":"e_1_3_2_47_2","doi-asserted-by":"crossref","unstructured":"A. Kirillov E. Mintun N. Ravi H. Mao C. Rolland L. Gustafson T. Xiao S. Whitehead A. C. Berg W. Y. Lo P. Dollar R. Girshick \u201cSegment anything \u201d in Proceedings of the IEEE\/CVF International Conference on Computer Vision (CVPR) (IEEE 2023) pp. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"e_1_3_2_48_2","doi-asserted-by":"crossref","unstructured":"G. Xu X. Wang X. Ding X. Yang \u201cIterative geometry encoding volume for stereo matching \u201d in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE 2023) pp. 21919\u201321928.","DOI":"10.1109\/CVPR52729.2023.02099"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","unstructured":"K. He X. Zhang S. Ren J. Sun \u201cDeep residual learning for image recognition \u201d in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE 2015) pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_50_2","first-page":"1","article-title":"Policy gradient and actor-critic learning in continuous time and space: Theory and algorithms","volume":"23","author":"Jia Y.","year":"2022","unstructured":"Y. Jia, X. Y. Zhou, Policy gradient and actor-critic learning in continuous time and space: Theory and algorithms. J. Mach. Learn. Res. 23, 1\u201350 (2022).","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_51_2","unstructured":"D. Silver G. Lever N. Heess T. Degris D. Wierstra M. Riedmiller \u201cDeterministic policy gradient algorithms \u201d in Proceedings of the International Conference on Machine Learning (ICML) (ICML 2014) pp. 387\u2013395."},{"key":"e_1_3_2_52_2","doi-asserted-by":"crossref","unstructured":"Y. H. Su A. Munawar A. Deguet A. Lewis K. Lindgren Y. Li R. H. Taylor G. S. Fischer B. Hannaford P. Kazanzides \u201cCollaborative Robotics toolkit (CRTK): Open software framework for surgical robotics research \u201d in Proceedings of the 2020 Fourth IEEE International Conference on Robotic Computing (IRC) (IEEE 2020) pp. 48\u201355.","DOI":"10.1109\/IRC.2020.00014"},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","unstructured":"G. Claudio F. Spindler F. Chaumette \u201cVision-based manipulation with the humanoid robot romeo\u201d in IEEE-RAS International Conference on Humanoid Robots (Humanoids) (IEEE 2016) pp. 286\u2013293.","DOI":"10.1109\/HUMANOIDS.2016.7803290"},{"key":"e_1_3_2_54_2","doi-asserted-by":"crossref","unstructured":"C. De Farias M. Adjigble B. Tamadazte R. Stolkin N. Marturi \u201cDual quaternion-based visual servoing for grasping moving objects\u201d in IEEE International Conference on Automation Science and Engineering (CASE) (IEEE. 2021) pp.151\u2013158.","DOI":"10.1109\/CASE49439.2021.9551631"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2023.3299533"}],"container-title":["Science Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/scirobotics.adt3093","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T18:00:12Z","timestamp":1752688812000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.adt3093"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,16]]},"references-count":54,"journal-issue":{"issue":"104","published-print":{"date-parts":[[2025,7,16]]}},"alternative-id":["10.1126\/scirobotics.adt3093"],"URL":"https:\/\/doi.org\/10.1126\/scirobotics.adt3093","relation":{},"ISSN":["2470-9476"],"issn-type":[{"value":"2470-9476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,16]]},"assertion":[{"value":"2024-09-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"eadt3093"}}