{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T07:07:27Z","timestamp":1788937647886,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":81,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032372512","type":"print"},{"value":"9783032372529","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-37252-9_3","type":"book-chapter","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:51:35Z","timestamp":1788936695000},"page":"37-56","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RoboStream: Weaving Spatio-Temporal Reasoning with\u00a0Memory in\u00a0Vision-Language Models for\u00a0Robotics"],"prefix":"10.1007","author":[{"given":"Yuzhi","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijue","family":"Bu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaoyang","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangye","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuzhao","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Armeni, I., et al.: 3D scene graph: a structure for unified semantics, 3D space, and camera. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5664\u20135673 (2019)","DOI":"10.1109\/ICCV.2019.00576"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Belkhale, S., et al.: RT-H: action hierarchies using language. arXiv preprint: arXiv:2403.01823 (2024)","DOI":"10.15607\/RSS.2024.XX.049"},{"key":"3_CR3","unstructured":"Brohan, A., et al.: RT-1: robotics transformer for real-world control at scale. arXiv preprint: arXiv:2212.06817 (2022)"},{"key":"3_CR4","unstructured":"Brohan, A., et al.: Do as i can, not as i say: grounding language in robotic affordances. In: Conference on Robot Learning, pp. 287\u2013318. PMLR (2023)"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Cai, W., et al.: SpatialBot: precise spatial understanding with vision language models. In: 2025 IEEE International Conference on Robotics and Automation (ICRA), pp. 9490\u20139498. IEEE (2025)","DOI":"10.1109\/ICRA55743.2025.11128671"},{"key":"3_CR6","unstructured":"Carion, N., et\u00a0al.: SAM 3: segment anything with concepts. arXiv preprint: arXiv:2511.16719 (2025)"},{"key":"3_CR7","unstructured":"Cheang, C., et\u00a0al.: GR-3 technical report. arXiv preprint: arXiv:2507.15493 (2025)"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Chen, B., et al.: SpatialVLM: endowing vision-language models with spatial reasoning capabilities. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14455\u201314465 (2024)","DOI":"10.1109\/CVPR52733.2024.01370"},{"key":"3_CR9","unstructured":"Chen, S., et al.: SpaceTools: tool-augmented spatial reasoning via double interactive RL. arXiv preprint: arXiv:2512.04069 (2025)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, A.C., et al.: SpatialRGPT: grounded spatial reasoning in vision-language models. In: Advances in Neural Information Processing Systems, vol. 37, pp. 135062\u2013135093 (2024)","DOI":"10.52202\/079017-4293"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Daxberger, E., et al.: MM-Spatial: exploring 3D spatial understanding in multimodal LLMs. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7395\u20137408 (2025)","DOI":"10.1109\/ICCV51701.2025.00694"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Ding, Y., et al.: Open6DOR: benchmarking open-instruction 6-DoF object rearrangement and a VLM-based approach. In: 2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7359\u20137366. IEEE (2024)","DOI":"10.1109\/IROS58592.2024.10802733"},{"key":"3_CR13","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint: arXiv:2010.11929 (2020)"},{"key":"3_CR14","unstructured":"Driess, D., et al.: PaLM-E: an embodied multimodal language model. In: International Conference on Machine Learning, pp. 8469\u20138488. PMLR (2023)"},{"key":"3_CR15","unstructured":"Du, Y., et al.: Vision-language models as success detectors. arXiv preprint: arXiv:2303.07280 (2023)"},{"key":"3_CR16","unstructured":"Fang, H., et al.: SAM2Act: integrating visual foundation model with a memory architecture for robotic manipulation. arXiv preprint: arXiv:2501.18564 (2025)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Fang, K., Liu, F., Abbeel, P., Levine, S.: MOKA: open-world robotic manipulation through mark-based visual prompting. Robot.: Sci. Syst. XX (2024)","DOI":"10.15607\/RSS.2024.XX.062"},{"key":"3_CR18","unstructured":"Gu, C., et\u00a0al.: ManualVLA: a unified VLA model for chain-of-thought manual generation and robotic manipulation. arXiv preprint: arXiv:2512.02013 (2025)"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Han, S., et al.: RoboCerebra: a large-scale benchmark for long-horizon robotic manipulation evaluation. In: The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2025)","DOI":"10.52202\/085713-4031"},{"key":"3_CR20","unstructured":"Han, Y., et al.: TIGeR: tool-integrated geometric reasoning in vision-language models for robotics. arXiv preprint: arXiv:2510.07181 (2025)"},{"key":"3_CR21","unstructured":"Hu, Y., Lin, F., Zhang, T., Yi, L., Gao, Y.: Look before you leap: Unveiling the power of GPT-4V in robotic vision-language planning. In: First Workshop on Vision-Language Models for Navigation and Manipulation at ICRA 2024 (2024)"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, F., Hu, Y., Wang, S., Gao, Y.: CoPa: general robotic manipulation through spatial constraints of parts with foundation models. In: 2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9488\u20139495. IEEE (2024)","DOI":"10.1109\/IROS58592.2024.10801352"},{"key":"3_CR23","unstructured":"Huang, S., et al.: A3VLM: actionable articulation-aware vision language model. In: Conference on Robot Learning, pp. 1675\u20131690. PMLR (2025)"},{"key":"3_CR24","unstructured":"Huang, W., Abbeel, P., Pathak, D., Mordatch, I.: Language models as zero-shot planners: extracting actionable knowledge for embodied agents. In: International Conference on Machine Learning, pp. 9118\u20139147. PMLR (2022)"},{"key":"3_CR25","unstructured":"Huang, W., Wang, C., Li, Y., Zhang, R., Fei-Fei, L.: ReKep: spatio-temporal reasoning of relational keypoint constraints for robotic manipulation. In: Conference on Robot Learning, pp. 4573\u20134602. PMLR (2025)"},{"key":"3_CR26","unstructured":"Huang, W., Wang, C., Zhang, R., Li, Y., Wu, J., Fei-Fei, L.: VoxPoser: composable 3D value maps for robotic manipulation with language models. In: Conference on Robot Learning, pp. 540\u2013562. PMLR (2023)"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Huang, W., et al.: Grounded decoding: guiding text generation with grounded models for embodied agents. In: Advances in Neural Information Processing Systems, vol. 36, pp. 59636\u201359661 (2023)","DOI":"10.52202\/075280-2606"},{"key":"3_CR28","unstructured":"Huang, W., et\u00a0al.: Inner monologue: embodied reasoning through planning with language models. In: Conference on Robot Learning, pp. 1769\u20131782. PMLR (2023)"},{"key":"3_CR29","unstructured":"Huang, Y., et al.: Thinking in dynamics: how multimodal large language models perceive, track, and reason dynamics in physical 4d world. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 33446\u201333456 (2026)"},{"key":"3_CR30","unstructured":"Hurst, A., et al.: GPT-4o system card. arXiv preprint: arXiv:2410.21276 (2024)"},{"issue":"2","key":"3_CR31","doi-asserted-by":"publisher","first-page":"3019","DOI":"10.1109\/LRA.2020.2974707","volume":"5","author":"S James","year":"2020","unstructured":"James, S., Ma, Z., Arrojo, D.R., Davison, A.J.: RLBench: the robot learning benchmark & learning environment. IEEE Robot. Autom. Lett. 5(2), 3019\u20133026 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"3_CR32","unstructured":"Jiang, Y., et al.: VIMA: general robot manipulation with multimodal prompts. In: International Conference on Machine Learning, pp. 14975\u201315022. PMLR (2023)"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Kapelyukh, I., Ren, Y., Alzugaray, I., Johns, E.: Dream2Real: zero-shot 3D object rearrangement with vision-language models. In: 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 4796\u20134803. IEEE (2024)","DOI":"10.1109\/ICRA57147.2024.10611220"},{"key":"3_CR34","unstructured":"Kim, M.J., et al.: OpenVLA: an open-source vision-language-action model. In: Conference on Robot Learning, pp. 2679\u20132713. PMLR (2025)"},{"key":"3_CR35","doi-asserted-by":"publisher","first-page":"e253","DOI":"10.1017\/S0140525X16001837","volume":"40","author":"BM Lake","year":"2017","unstructured":"Lake, B.M., Ullman, T.D., Tenenbaum, J.B., Gershman, S.J.: Building machines that learn and think like people. Behav. Brain Sci. 40, e253 (2017)","journal-title":"Behav. Brain Sci."},{"key":"3_CR36","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s42256-025-01168-7","volume":"8","author":"X Li","year":"2026","unstructured":"Li, X., et al.: What matters in building vision-language-action models for generalist robots. Nat. Mach. Intell. 8, 158\u2013172 (2026). https:\/\/doi.org\/10.1038\/s42256-025-01168-7","journal-title":"Nat. Mach. Intell."},{"key":"3_CR37","unstructured":"Li, X., et al.: Evaluating real-world robot manipulation policies in simulation. In: Conference on Robot Learning, pp. 3705\u20133728. PMLR (2025)"},{"key":"3_CR38","unstructured":"Li, Y., et al.: TS-DP: reinforcement speculative decoding for temporal adaptive diffusion policy acceleration. arXiv preprint: arXiv:2512.15773 (2025)"},{"key":"3_CR39","unstructured":"Li, Y., et al.: ElegantVLA: learning when to think for efficient vision-language-action models. arXiv preprint: arXiv:2605.29438 (2026)"},{"key":"3_CR40","unstructured":"Li, Y., et al.: SP-VLA: a joint model scheduling and token pruning approach for VLA model acceleration. arXiv preprint: arXiv:2506.12723 (2025)"},{"key":"3_CR41","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: PRANCE: joint token-optimization and structural channel-pruning for adaptive vit inference. IEEE Trans. Pattern Anal. Mach. Intell. (2025)","DOI":"10.1109\/TPAMI.2025.3605239"},{"key":"3_CR42","unstructured":"Li, Y., et\u00a0al.: HAMSTER: hierarchical action models for open-world robot manipulation. In: The Thirteenth International Conference on Learning Representations (2025)"},{"key":"3_CR43","doi-asserted-by":"crossref","unstructured":"Liang, J., et al.: Code as policies: language model programs for embodied control. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 9493\u20139500. IEEE (2023)","DOI":"10.1109\/ICRA48891.2023.10160591"},{"key":"3_CR44","unstructured":"Liu, H., Chen, A., Zhu, Y., Swaminathan, A., Kolobov, A., Cheng, C.A.: Interactive robot learning from verbal correction. In: 2nd Workshop on Language and Robot Learning: Language as Grounding (2023)"},{"key":"3_CR45","unstructured":"Mao, W., et al.: RoboMatrix: a skill-centric hierarchical framework for scalable robot task planning and execution in open-world. arXiv preprint: arXiv:2412.00171 (2024)"},{"key":"3_CR46","doi-asserted-by":"crossref","unstructured":"Mu, Y., et al.: EmbodiedGPT: vision-language pre-training via embodied chain of thought. In: Advances in Neural Information Processing Systems, vol. 36, pp. 25081\u201325094 (2023)","DOI":"10.52202\/075280-1090"},{"key":"3_CR47","doi-asserted-by":"crossref","unstructured":"Qi, Z., et\u00a0al.: SoFar: language-grounded orientation bridges spatial reasoning and object manipulation. In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2025)","DOI":"10.52202\/085713-2565"},{"key":"3_CR48","doi-asserted-by":"crossref","unstructured":"Qin, Y., et al.: RoboFactory: exploring embodied agent collaboration with compositional constraints. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10075\u201310085 (2025)","DOI":"10.1109\/ICCV51701.2025.00939"},{"key":"3_CR49","doi-asserted-by":"crossref","unstructured":"Qu, D., et\u00a0al.: SpatialVLA: exploring spatial representations for visual-language-action model. arXiv preprint: arXiv:2501.15830 (2025)","DOI":"10.15607\/RSS.2025.XXI.011"},{"key":"3_CR50","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"3_CR51","unstructured":"Ray, A., et al.: SAT: dynamic spatial aptitude training for multimodal language models. In: Second Conference on Language Modeling (2025)"},{"key":"3_CR52","doi-asserted-by":"crossref","unstructured":"Sharma, P., et al.: Correcting robot plans with natural language feedback. arXiv preprint: arXiv:2204.05186 (2022)","DOI":"10.15607\/RSS.2022.XVIII.065"},{"key":"3_CR53","unstructured":"Shi, H., et al.: MemoryVLA: perceptual-cognitive memory in vision-language-action models for robotic manipulation. In: International Conference on Learning Representations (2026). https:\/\/openreview.net\/forum?id=54U3XHf7qq. Accessed 16 July 2026"},{"key":"3_CR54","doi-asserted-by":"crossref","unstructured":"Shi, L., et al.: Yell at your robot: improving on-the-fly from language corrections. Robot.: Sci. Syst. XX (2024)","DOI":"10.15607\/RSS.2024.XX.025"},{"key":"3_CR55","unstructured":"Shi, L.X., et al.: Hi robot: open-ended instruction following with hierarchical vision-language-action models. In: International Conference on Machine Learning, pp. 54919\u201354933. PMLR (2025)"},{"key":"3_CR56","unstructured":"Shridhar, M., Manuelli, L., Fox, D.: Perceiver-actor: a multi-task transformer for robotic manipulation. In: Conference on Robot Learning, pp. 785\u2013799. PMLR (2023)"},{"key":"3_CR57","doi-asserted-by":"crossref","unstructured":"Singh, I., et al.: ProgPrompt: generating situated robot task plans using large language models. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 11523\u201311530. IEEE (2023)","DOI":"10.1109\/ICRA48891.2023.10161317"},{"key":"3_CR58","doi-asserted-by":"crossref","unstructured":"Song, C.H., Blukis, V., Tremblay, J., Tyree, S., Su, Y., Birchfield, S.: RoboSpatial: teaching spatial understanding to 2d and 3d vision-language models for robotics. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 15768\u201315780 (2025)","DOI":"10.1109\/CVPR52734.2025.01470"},{"key":"3_CR59","unstructured":"Tan, H., et\u00a0al.: RoboOs: a hierarchical embodied framework for cross-embodiment and multi-agent collaboration. arXiv preprint: arXiv:2505.03673 (2025)"},{"key":"3_CR60","unstructured":"Team, O.M., et al.: Octo: an open-source generalist robot policy. arXiv preprint: arXiv:2405.12213 (2024)"},{"key":"3_CR61","doi-asserted-by":"crossref","unstructured":"Wald, J., Dhamo, H., Navab, N., Tombari, F.: Learning 3D semantic scene graphs from 3D indoor reconstructions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3961\u20133970 (2020)","DOI":"10.1109\/CVPR42600.2020.00402"},{"key":"3_CR62","doi-asserted-by":"crossref","unstructured":"Wang, X., Ma, W., Zhang, T., de\u00a0Melo, C.M., Chen, J., Yuille, A.: Spatial457: a diagnostic benchmark for 6d spatial reasoning of large multimodal models. In: 2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 24669\u201324679. IEEE (2025)","DOI":"10.1109\/CVPR52734.2025.02297"},{"key":"3_CR63","doi-asserted-by":"crossref","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: Advances in Neural Information Processing Systems, vol. 35, pp. 24824\u201324837 (2022)","DOI":"10.52202\/068431-1800"},{"key":"3_CR64","unstructured":"Wen, J., Zhu, Y., Li, J., Tang, Z., Shen, C., Feng, F.: DexVLA: vision-language model with plug-in diffusion expert for general robot control. In: Conference on Robot Learning, pp. 3094\u20133114. PMLR (2025)"},{"key":"3_CR65","unstructured":"Wen, K., et al.: DynamicVerse: a physically-aware multimodal framework for 4D world modeling. In: Advances in Neural Information Processing Systems, vol.\u00a038 (2025). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2025\/hash\/9c20f16b05f5e5e70fa07e2a4364b80e-Abstract-Conference.html. Accessed 16 July 2026"},{"key":"3_CR66","doi-asserted-by":"crossref","unstructured":"Wu, J., et al.: TidyBot: personalized robot assistance with large language models. In: 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3546\u20133553. IEEE (2023)","DOI":"10.1109\/IROS55552.2023.10341577"},{"key":"3_CR67","unstructured":"Yang, A., et\u00a0al.: Qwen3 technical report. arXiv preprint: arXiv:2505.09388 (2025)"},{"key":"3_CR68","unstructured":"Yang, J., Zhang, H., Li, F., Zou, X., Li, C., Gao, J.: Set-of-mark prompting unleashes extraordinary visual grounding in GPT-4V. arXiv preprint: arXiv:2310.11441 (2023)"},{"key":"3_CR69","doi-asserted-by":"crossref","unstructured":"Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., Zhao, H.: Depth anything: unleashing the power of large-scale unlabeled data. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.00987"},{"key":"3_CR70","unstructured":"Yang, Y., Sun, J., Kou, S., Wang, Y., Deng, Z.: LoHoVLA: a unified vision-language-action model for long-horizon embodied tasks. arXiv preprint: arXiv:2506.00411 (2025)"},{"key":"3_CR71","unstructured":"Yu, W., et al.: Language to rewards for robotic skill synthesis. In: Conference on Robot Learning, pp. 374\u2013404. PMLR (2023)"},{"key":"3_CR72","unstructured":"Yuan, W., et al.: RoboPoint: a vision-language model for spatial affordance prediction in robotics. In: Conference on Robot Learning, pp. 4005\u20134020. PMLR (2025)"},{"key":"3_CR73","unstructured":"Zawalski, M., Chen, W., Pertsch, K., Mees, O., Finn, C., Levine, S.: Robotic control via embodied chain-of-thought reasoning. In: Conference on Robot Learning, pp. 3157\u20133181. PMLR (2025)"},{"key":"3_CR74","unstructured":"Zeng, A., et al.: Socratic models: composing zero-shot multimodal reasoning with language. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"3_CR75","doi-asserted-by":"crossref","unstructured":"Zhang, K., Yin, Z.H., Ye, W., Gao, Y.: Learning manipulation skills through robot chain-of-thought with sparse failure guidance. In: 2025 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8012\u20138018. IEEE (2025)","DOI":"10.1109\/IROS60139.2025.11247602"},{"key":"3_CR76","doi-asserted-by":"crossref","unstructured":"Zhao, Q., et al.: CoT-VLA: visual chain-of-thought reasoning for vision-language-action models. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 1702\u20131713 (2025)","DOI":"10.1109\/CVPR52734.2025.00166"},{"key":"3_CR77","unstructured":"Zhen, H., et al.: 3D-VLA: a 3D vision-language-action generative world model. In: International Conference on Machine Learning, pp. 61229\u201361245. PMLR (2024)"},{"key":"3_CR78","doi-asserted-by":"crossref","unstructured":"Zhou, E., et\u00a0al.: RoboRefer: towards spatial referring with reasoning in vision-language models for robotics. In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2025)","DOI":"10.52202\/085713-0957"},{"key":"3_CR79","doi-asserted-by":"crossref","unstructured":"Zhou, E., et al.: Code-as-monitor: constraint-aware visual programming for reactive and proactive robotic failure detection. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 6919\u20136929 (2025)","DOI":"10.1109\/CVPR52734.2025.00649"},{"key":"3_CR80","doi-asserted-by":"crossref","unstructured":"Zhu, K., et al.: DexFlyWheel: a scalable and self-improving data generation framework for dexterous manipulation. In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2025)","DOI":"10.52202\/085713-0150"},{"key":"3_CR81","unstructured":"Zitkovich, B., et\u00a0al.: RT-2: vision-language-action models transfer web knowledge to robotic control. In: Conference on Robot Learning, pp. 2165\u20132183. PMLR (2023)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37252-9_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:51:54Z","timestamp":1788936714000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37252-9_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032372512","9783032372529"],"references-count":81,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37252-9_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"10 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}