{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:06:24Z","timestamp":1790075184929,"version":"4.0.1"},"reference-count":112,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>World models are becoming a crucial foundation for embodied intelligence, which transforms sensor observations into internal state representations that support prediction, planning, and control. However, existing research is scattered across different fields (autonomous driving, robotic manipulation, generative interactive environments, and physical intelligence), and lacks a description of the role of sensors in defining the observable world, shaping state representations, and influencing decision-making. This paper proposes a unified Sensor\u2013State\u2013Decision (SSD) framework to describe sensor-driven world models. Specifically, the Sensor-Layer acquires raw physical observation data, the State-Layer transforms it into a structured world state representation, and the Decision-Layer outputs the final executable decision. Furthermore, it proposes that the far-field and near-field mechanisms describe the relative positions between complete SSD instances in the SSD hierarchy. That is, higher layers constrain lower layers through goal injection, and lower layers correct higher layers through result feedback. Based on this framework, we synthesize representative architectures, applications, datasets, and benchmarks, particularly long-tail scenarios in the Far-Field and Near-Field domains. Finally, we summarize the current challenges. The SSD perspective promises to provide a new structured analytical framework for designing robust, physically based, and deployable embodied world models in the real world.<\/jats:p>","DOI":"10.3390\/s26185944","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:21:48Z","timestamp":1790072508000},"page":"5944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sensor-Driven World Models for Embodied Intelligence: A Survey of Sensor\u2013State\u2013Decision Modeling Across Far-Field and Near-Field Regimes"],"prefix":"10.3390","volume":"26","author":[{"given":"Yi","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7346-2209","authenticated-orcid":false,"given":"Beichen","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"ref_1","first-page":"2450","article-title":"Recurrent World Models Facilitate Policy Evolution","volume":"Volume 31","author":"Ha","year":"2018","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_2","unstructured":"Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., and Davidson, J. (2019). Learning latent dynamics for planning from pixels. International Conference on Machine Learning, PMLR."},{"key":"ref_3","unstructured":"Hafner, D., Lillicrap, T., Ba, J., and Norouzi, M. (2020, January 26\u201330). Dream to Control: Learning Behaviors by Latent Imagination. Proceedings of the International Conference on Learning Representations, Online."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1038\/s41586-025-08744-2","article-title":"Mastering diverse control tasks through world models","volume":"640","author":"Hafner","year":"2025","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1038\/s41586-020-03051-4","article-title":"Mastering atari, go, chess and shogi by planning with a learned model","volume":"588","author":"Schrittwieser","year":"2020","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1038\/s42256-025-01054-2","article-title":"Model-based reinforcement learning for ultrasound-driven autonomous microrobots","volume":"7","author":"Medany","year":"2025","journal-title":"Nat. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1038\/s44172-025-00474-7","article-title":"World model-based end-to-end scene generation for accident anticipation in autonomous driving","volume":"4","author":"Guan","year":"2025","journal-title":"Commun. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1038\/s41586-025-08600-3","article-title":"World and human action models towards gameplay ideation","volume":"638","author":"Kanervisto","year":"2025","journal-title":"Nature"},{"key":"ref_9","unstructured":"Bruce, J., Dennis, M.D., Edwards, A., Parker-Holder, J., Shi, Y., Hughes, E., Lai, M., Mavalankar, A., Steigerwald, R., and Apps, C. (2024, January 21\u201327). Genie: Generative interactive environments. Proceedings of the Forty-First International Conference on Machine Learning, Vienna, Austria."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/d41586-026-00820-5","article-title":"\u2019World models\u2019 are AI\u2019s latest sensation: What are they and what can they do?","volume":"653","author":"Castelvecchi","year":"2026","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, Z., Chai, Y., Anguelov, D., Zhou, Y., Sun, P., Erhan, D., Rafferty, S., and Kretzschmar, H. (2020, January 14\u201319). SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR42600.2020.01113"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mur-Labadia, L., Muckley, M., Bar, A., Assran, M., Sinha, K., Rabbat, M., LeCun, Y., Ballas, N., and Bardes, A. (2026). V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning. arXiv.","DOI":"10.1007\/978-3-032-37369-4_36"},{"key":"ref_13","unstructured":"Nam, H., Le Lidec, Q., Maes, L., LeCun, Y., and Balestriero, R. (2026, January 6\u201311). Causal-JEPA: Learning World Models Through Object-Level Latent Masking. Proceedings of the Forty-Third International Conference on Machine Learning, Seoul, Republic of Korea."},{"key":"ref_14","unstructured":"Yang, S., Du, Y., Ghasemipour, K., Tompson, J., Kaelbling, L.P., Schuurmans, D., and Abbeel, P. (2024, January 7\u201311). Learning Interactive Real-World Simulators. Proceedings of the Twelfth International Conference on Learning Representations, Vienna, Austria."},{"key":"ref_15","unstructured":"Huang, W., Chao, Y.W., Mousavian, A., Liu, M.Y., Fox, D., Mo, K., and Li, F.-F. (2026, January 3\u20137). PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Denver, CO, USA."},{"key":"ref_16","unstructured":"Ye, S., Ge, Y., Zheng, K., Gao, S., Yu, S., Kurian, G., Indupuru, S., Tan, Y.L., Zhu, C., and Xiang, J. (2026, January 27). World Action Models Are Zero-Shot Policies. Proceedings of the ICLR 2026 Workshop on World Models: Understanding, Modelling and Scaling, Rio de Janeiro, Brazil."},{"key":"ref_17","unstructured":"Jang, J., Ye, S., Lin, Z., Xiang, J., Bjorck, J., Fang, Y., Hu, F., Huang, S., Kundalia, K., and Lin, Y.C. (2025, January 27\u201330). DreamGen: Unlocking Generalization in Robot Learning through Video World Models. Proceedings of the 9th Conference on Robot Learning, Seoul, Republic of Korea."},{"key":"ref_18","unstructured":"Bjorck, J., Casta\u00f1eda, F., Cherniadev, N., Da, X., Ding, R., Fan, L.J., Fang, Y., Fox, D., Hu, F., and Huang, S. (2025). GR00T N1: An Open Foundation Model for Generalist Humanoid Robots. arXiv."},{"key":"ref_19","unstructured":"Higuera, C., Arnaud, S., Boots, B., Mukadam, M., Hogan, F.R., and Meier, F. (2026). Visuo-Tactile World Models. arXiv."},{"key":"ref_20","unstructured":"Hu, A., Russell, L., Yeo, H., Murez, Z., Fedoseev, G., Kendall, A., Shotton, J., and Corrado, G. (2023). GAIA-1: A Generative World Model for Autonomous Driving. arXiv."},{"key":"ref_21","first-page":"55","article-title":"DriveDreamer: Towards Real-World-Drive World Models for Autonomous Driving","volume":"Volume 15106","author":"Wang","year":"2024","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2024"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, G., Wang, X., Zhu, Z., Chen, X., Huang, G., Bao, X., and Wang, X. (March, January 25). DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation. Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA.","DOI":"10.1609\/aaai.v39i10.33130"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bogdoll, D., Yang, Y., Joseph, T., Yazgan, M., and Z\u00f6llner, J.M. (2025, January 22\u201325). MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations. Proceedings of the 2025 IEEE Intelligent Vehicles Symposium, Cluj-Napoca, Romania.","DOI":"10.1109\/IV64158.2025.11097718"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tian, X., Jiang, T., Yun, L., Mao, Y., Yang, H., Wang, Y., Wang, Y., and Zhao, H. (2023, January 10\u201316). Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving. Proceedings of the Advances in Neural Information Processing Systems, New Orleans, LA, USA.","DOI":"10.52202\/075280-2809"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, G., Wang, Y., Wang, X., Zhu, Z., Yu, T., Huang, G., Zai, Y., Jiao, J., Xue, C., and Wang, X. (2026). UniDriveDreamer: A Single-Stage Multimodal World Model for Autonomous Driving. arXiv.","DOI":"10.1007\/978-3-032-37314-4_20"},{"key":"ref_26","unstructured":"Ren, X., Lu, Y., Cao, T., Gao, R., Huang, S., Sabour, A., Shen, T., Pfaff, T., Wu, J.Z., and Chen, R. (2025). Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models. arXiv."},{"key":"ref_27","unstructured":"Basant, A., Kar, A., Paschalidou, D., Wei, F., Ferroni, F., Cobo, G.G., Turki, H., Ling, H., Seo, J., and Lucas, J. (2026). NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation. arXiv."},{"key":"ref_28","unstructured":"Agarwal, N., Ali, A., Bala, M., Balaji, Y., Barker, E., Cai, T., Chattopadhyay, P., Chen, Y., Cui, Y., and Ding, Y. (2025). Cosmos World Foundation Model Platform for Physical AI. arXiv."},{"key":"ref_29","unstructured":"Agarwal, N., Ali, A., Allen, J., Antolini, M., Aubame, A., Azzolini, A., Bai, J., Bala, M., and Balaji, Y. (2026). Cosmos 3: Omnimodal World Models for Physical AI. arXiv."},{"key":"ref_30","unstructured":"LeCun, Y. (2022). A Path Towards Autonomous Machine Intelligence, OpenReview. Available online: https:\/\/openreview.net\/forum?id=BZ5a1r-kVsf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-20077-9_1","article-title":"BEVFormer: Learning Bird\u2019s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers","volume":"Volume 13669","author":"Li","year":"2022","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2022"},{"key":"ref_32","unstructured":"Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., and Altman, S. (2024). GPT-4 Technical Report. arXiv."},{"key":"ref_33","unstructured":"Zitkovich, B., Yu, T., Xu, S., Xu, P., Xiao, T., Xia, F., Wu, J., Wohlhart, P., Welker, S., and Wahid, A. (2023, January 6\u20139). RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. Proceedings of the 7th Conference on Robot Learning, Atlanta, GA, USA."},{"key":"ref_34","unstructured":"Kim, M.J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E.P., Sanketi, P.R., and Vuong, Q. (2024, January 6\u20139). OpenVLA: An Open-Source Vision-Language-Action Model. Proceedings of the 8th Conference on Robot Learning, Munich, Germany."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, Y., Ma, Y., Feng, H., Cheng, Y., and Han, Z. (2025). Minimizing Hallucinations and Communication Costs: Adversarial Debate and Voting Mechanisms in LLM-Based Multi-Agents. Appl. Sci., 15.","DOI":"10.3390\/app15073676"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lan, X., Han, Z., Cheng, Y., Sheng, L., Feng, J., Gao, C., and Li, Y. (2025). DORIS: Depression Detection on Social Media with Large Language Models. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2025.emnlp-industry.151"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Ma, Y., Wang, J., Feng, H., Zhang, Y., and Yang, Y. (2026). Large Language Model Agents for Recommender Systems: Bridging Behavior and Semantics with Long-Short Term Interest Modeling. Electronics, 15.","DOI":"10.3390\/electronics15163591"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Du, K., Wang, B., Zhang, C., Cheng, Y., Lan, Q., Sang, H., Cheng, Y., Yao, J., Liu, X., and Qiao, Y. (2025). PrefillOnly: An Inference Engine for Prefill-Only Workloads in Large Language Model Applications. Proceedings of the 31st ACM SIGOPS Symposium on Operating Systems Principles (SOSP), Seoul, Republic of Korea, 13\u201316 October 2025, Association for Computing Machinery.","DOI":"10.1145\/3731569.3764834"},{"key":"ref_39","unstructured":"Zhu, Z., Wang, X., Zhao, W., Min, C., Li, B., Deng, N., Dou, M., Wang, Y., Shi, B., and Wang, K. (2025). Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/978-3-031-72624-8_4","article-title":"OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving","volume":"Volume 15071","author":"Zheng","year":"2025","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2024"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0004-3702(98)00023-X","article-title":"Planning and acting in partially observable stochastic domains","volume":"101","author":"Kaelbling","year":"1998","journal-title":"Artif. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108796","DOI":"10.1016\/j.patcog.2022.108796","article-title":"3D object detection for autonomous driving: A survey","volume":"130","author":"Qian","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1007\/s11263-023-01790-1","article-title":"3D object detection for autonomous driving: A comprehensive survey","volume":"131","author":"Mao","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Qian, H., Wang, M., Zhu, M., and Wang, H. (2025). A Review of Multi-Sensor Fusion in Autonomous Driving. Sensors, 25.","DOI":"10.3390\/s25196033"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TRO.2016.2597321","article-title":"On-manifold preintegration for real-time visual\u2013inertial odometry","volume":"33","author":"Forster","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bloesch, M., Hutter, M., Hoepflinger, M.A., Leutenegger, S., Gehring, C., Remy, C.D., and Siegwart, R. (2012, January 9\u201313). State Estimation for Legged Robots: Consistent Fusion of Leg Kinematics and IMU. Proceedings of the Robotics: Science and Systems, Sydney, Australia.","DOI":"10.15607\/RSS.2012.VIII.003"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yang, S., Zhang, Z., Bokser, B., and Manchester, Z. (2023, January 1\u20135). Multi-IMU proprioceptive odometry for legged robots. Proceedings of the 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA.","DOI":"10.1109\/IROS55552.2023.10342061"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5564","DOI":"10.1109\/TRO.2025.3608686","article-title":"Tactile Robotics: An Outlook","volume":"41","author":"Luo","year":"2025","journal-title":"IEEE Trans. Robot."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Montanaro, U., Attanasio, C.A., Pietroni, S., and Studi, A. (2026). Sensors in Self-Driving Vehicles: A Detailed Literature Review and New Trends. Sensors, 26.","DOI":"10.3390\/s26072153"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 14\u201319). nuScenes: A Multimodal Dataset for Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Gupta, S., Kanjani, J., Li, M., Ferroni, F., Hays, J., Ramanan, D., and Kong, S. (2023, January 3\u20137). Far3Det: Towards Far-Field 3D Detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV56688.2023.00076"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4606","DOI":"10.1109\/TIV.2024.3380244","article-title":"4D mmWave Radar for Autonomous Driving Perception: A Comprehensive Survey","volume":"9","author":"Fan","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_53","unstructured":"Paek, D.H., Kong, S.H., and Wijaya, K.T. (December, January 28). K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions. Proceedings of the 36th International Conference on Neural Information Processing Systems, New Orleans, LA, USA."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Nabati, R., and Qi, H. (2021, January 5\u20139). CenterFusion: Center-Based Radar and Camera Fusion for 3D Object Detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Online.","DOI":"10.1109\/WACV48630.2021.00157"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Camurri, M., Ramezani, M., Nobili, S., and Fallon, M. (2020). Pronto: A Multi-Sensor State Estimator for Legged Robots in Real-World Scenarios. Front. Robot. AI, 7.","DOI":"10.3389\/frobt.2020.00068"},{"key":"ref_56","unstructured":"Lin, T.Y., Li, T., Tong, W., and Ghaffari, M. (2024). Proprioceptive Invariant Robot State Estimation. arxiv."},{"key":"ref_57","first-page":"2226","article-title":"DayDreamer: World Models for Physical Robot Learning","volume":"Volume 205","author":"Wu","year":"2023","journal-title":"Proceedings of the 6th Conference on Robot Learning, Auckland, New Zealand, 14\u201318 December 2023"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yuan, W., Dong, S., and Adelson, E.H. (2017). GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force. Sensors, 17.","DOI":"10.3390\/s17122762"},{"key":"ref_59","unstructured":"Huang, J., Huang, G., Zhu, Z., Ye, Y., and Du, D. (2022). BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liu, Z., Tang, H., Amini, A., Yang, X., Mao, H., Rus, D., and Han, S. (June, January 29). BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird\u2019s-Eye View Representation. Proceedings of the 2023 IEEE International Conference on Robotics and Automation, London, UK.","DOI":"10.1109\/ICRA48891.2023.10160968"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., and Wang, W. (2023, January 18\u201322). Planning-Oriented Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01712"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., and Geiger, A. (2019, January 15\u201320). Occupancy Networks: Learning 3D Reconstruction in Function Space. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00459"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Tong, W., Sima, C., Wang, T., Chen, L., Wu, S., Deng, H., Gu, Y., Lu, L., Luo, P., and Lin, D. (2023, January 1\u20136). Scene as Occupancy. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00772"},{"key":"ref_64","unstructured":"Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., and Kipf, T. (2020, January 6\u201312). Object-Centric Learning with Slot Attention. Proceedings of the Advances in Neural Information Processing Systems, Online."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ferraro, S., Mazzaglia, P., Verbelen, T., and Dhoedt, B. (2025). FOCUS: Object-Centric World Models for Robotic Manipulation. Front. Neurorobotics, 19.","DOI":"10.3389\/fnbot.2025.1585386"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"68082","DOI":"10.52202\/079017-2173","article-title":"ivideogpt: Interactive videogpts are scalable world models","volume":"37","author":"Wu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Gao, S., Yang, J., Chen, L., Chitta, K., Qiu, Y., Geiger, A., Zhang, J., and Li, H. (2024, January 10\u201315). Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada.","DOI":"10.52202\/079017-2906"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Yang, Y., Feng, H., Cheng, Y., and Han, Z. (2024). Emotion-Aware Scene Adaptation: A Bandwidth-Efficient Approach for Generating Animated Shorts. Sensors, 24.","DOI":"10.3390\/s24051660"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Xu, X., Dong, S., Xu, T., Ding, L., Wang, J., Jiang, P., Song, L., and Li, J. (2023). FusionRCNN: LiDAR-Camera Fusion for Two-Stage 3D Object Detection. Remote. Sens., 15.","DOI":"10.3390\/rs15071839"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Bai, X., Hu, Z., Zhu, X., Huang, Q., Chen, Y., Fu, H., and Tai, C.L. (2022, January 18\u201324). TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00116"},{"key":"ref_71","unstructured":"Zhou, S., Du, Y., Chen, J., Li, Y., Yeung, D.Y., and Gan, C. (, January 21\u201327). RoboDreamer: Learning Compositional World Models for Robot Imagination. Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhu, J.Y., Tedrake, R., and Torralba, A. (2019, January 15\u201320). Connecting Touch and Vision via Cross-Modal Prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01086"},{"key":"ref_73","unstructured":"Huang, Y., Lin, P., Li, W., Li, D., Li, J., Jiang, J., Xiao, C., and Jiao, Z. (2026). Tactile-Force Alignment in Vision-Language-Action Models for Force-Aware Manipulation. arXiv."},{"key":"ref_74","unstructured":"Hafner, D., Lee, K.H., Fischer, I., and Abbeel, P. (December, January 28). Deep hierarchical planning from pixels. Proceedings of the 36th Conference on Neural Information Processing Systems, New Orleans, LA, USA."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Sun, J., Xie, Y., Chen, L., Zhou, X., and Bao, H. (2021, January 19\u201325). NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR46437.2021.01534"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, H., Zhai, H., Ming, Y., Liu, Y., and Zhang, G. (2022, January 17\u201321). Vox-Fusion: Dense Tracking and Mapping with Voxel-Based Neural Implicit Representation. Proceedings of the 2022 IEEE International Symposium on Mixed and Augmented Reality, Singapore.","DOI":"10.1109\/ISMAR55827.2022.00066"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Yang, Z., Chen, Y., Wang, J., Manivasagam, S., Ma, W.C., Yang, A.J., and Urtasun, R. (2023, January 17\u201324). UniSim: A Neural Closed-Loop Sensor Simulator. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00140"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"3930","DOI":"10.1109\/LRA.2022.3146945","article-title":"TACTO: A Fast, Flexible, and Open-Source Simulator for High-Resolution Vision-Based Tactile Sensors","volume":"7","author":"Wang","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P. (2017, January 24\u201328). Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202133"},{"key":"ref_80","first-page":"1326","article-title":"A User\u2019s Guide to Calibrating Robotic Simulators","volume":"Volume 155","author":"Mehta","year":"2021","journal-title":"Proceedings of the 2020 Conference on Robot Learning, Online, 16\u201318 November 2020"},{"key":"ref_81","unstructured":"Caesar, H., Kabzan, J., Tan, K.S., Fong, W.K., Wolff, E., Lang, A., Fletcher, L., Beijbom, O., and Omari, S. (2021). nuPlan: A Closed-Loop ML-Based Planning Benchmark for Autonomous Vehicles. arXiv."},{"key":"ref_82","unstructured":"Gu, J., Xiang, F., Li, X., Ling, Z., Liu, X., Mu, T., Tang, Y., Tao, S., Wei, X., and Yao, Y. (2023, January 1\u20135). ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills. Proceedings of the International Conference on Learning Representations, Kigali, Rwanda."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhao, Y., Liu, Y., Jia, F., Wang, Y., Luo, C., Zhang, C., Wang, T., Sun, X., and Zhang, X. (2024, January 16\u20132). Panacea: Panoramic and Controllable Video Generation for Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00659"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhao, Y., Liu, Y., Huang, B., Jia, F., Wang, Y., Zhang, C., Wang, T., Sun, X., and Zhang, X. (2024). Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving. arXiv.","DOI":"10.1109\/CVPR52733.2024.00659"},{"key":"ref_85","unstructured":"Gao, R., Chen, K., Xie, E., Hong, L., Li, Z., Yeung, D.Y., and Xu, Q. (2024, January 7\u20131). MagicDrive: Street View Generation with Diverse 3D Geometry Control. Proceedings of the International Conference on Learning Representations, Vienna, Austria."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Gao, R., Chen, K., Xiao, B., Hong, L., Li, Z., and Xu, Q. (2025, January 19\u201320). MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Honolulu, HI, USA.","DOI":"10.1109\/ICCV51701.2025.02612"},{"key":"ref_87","first-page":"329","article-title":"WoVoGen: World Volume-Aware Diffusion for Controllable Multi-Camera Driving Scene Generation","volume":"Volume 15138","author":"Lu","year":"2025","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2024"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Wang, Y., He, J., Fan, L., Li, H., Chen, Y., and Zhang, Z. (2024, January 16\u201322). Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01397"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Yang, J., Chitta, K., Gao, S., Chen, L., Shao, Y., Jia, X., Li, H., Geiger, A., Yue, X., and Chen, L. (2025, January 2\u20137). ReSim: Reliable World Simulation for Autonomous Driving. Proceedings of the Advances in Neural Information Processing Systems, San Diego, CA, USA.","DOI":"10.52202\/085713-5591"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s11548-015-1305-z","article-title":"Surgical robotics beyond enhanced dexterity instrumentation: A survey of machine learning techniques and their role in intelligent and autonomous surgical actions","volume":"11","author":"Kassahun","year":"2016","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wang, Z.Y., Liu, Q., Sun, S., Wang, K., Chellappa, R., Zhou, Z., Yuille, A., Zhu, L., and Zhang, Y.D. (2025, January 19\u201320). Medical World Model. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Honolulu, HI, USA.","DOI":"10.1109\/ICCV51701.2025.00779"},{"key":"ref_92","unstructured":"He, Y., Guo, P., Xu, M., Li, Z., Myronenko, A., Imans, D., Liu, B., Yang, D., Gu, M., and Ji, Y. (2025). Cosmos-H-Surgical: Learning Surgical Robot Policies from Videos via World Modeling. arXiv."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1109\/LRA.2020.2974707","article-title":"RLBench: The Robot Learning Benchmark & Learning Environment","volume":"5","author":"James","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_94","unstructured":"(2024, January 13\u201317). Open X-Embodiment Collaboration. Open X-Embodiment: Robotic Learning Datasets and RT-X Models. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Fang, H.S., Fang, H., Tang, Z., Liu, J., Wang, C., Wang, J., Zhu, H., and Lu, C. (2024, January 13\u201317). RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10611615"},{"key":"ref_96","unstructured":"Zhou, H., Liu, C., Wu, Y., Wu, J., Wu, Z., Lee, Y.C., Martuscelli, S., Salcudean, S.E., Fischer, G.S., and Kazanzides, P. (2026). SurgSync: Time-Synchronized Multi-Modal Data Collection Framework and Dataset for Surgical Robotics. arXiv."},{"key":"ref_97","first-page":"406","article-title":"CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving","volume":"Volume 13698","author":"Li","year":"2022","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2022"},{"key":"ref_98","unstructured":"Li, C., Zhang, R., Wong, J., Gokmen, C., Srivastava, S., Mart\u00edn-Mart\u00edn, R., Wang, C., Levine, G., Lingelbach, M., and Sun, J. (2023). Behavior-1k: A benchmark for embodied ai with 1000 everyday activities and realistic simulation. Proceedings of the Conference on Robot Learning, PMLR."},{"key":"ref_99","first-page":"1","article-title":"CARLA: An Open Urban Driving Simulator","volume":"Volume 78","author":"Dosovitskiy","year":"2017","journal-title":"Proceedings of the 1st Annual Conference on Robot Learning, Mountain View, CA, USA, 13\u201315 November 2017"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., and Caine, B. (2020, January 13\u201319). Scalability in Perception for Autonomous Driving: Waymo Open Dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref_102","unstructured":"Wilson, B., Qi, W., Agarwal, T., Lambert, J., Singh, J., Khandelwal, S., Pan, B., Kumar, R., Hartnett, A., and Pontes, J.K. (2021). Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting. Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, Curran Associates."},{"key":"ref_103","first-page":"885","article-title":"RoboNet: Large-Scale Multi-Robot Learning","volume":"Volume 100","author":"Dasari","year":"2020","journal-title":"Proceedings of the 3rd Conference on Robot Learning (CoRL 2019), Osaka, Japan, 30 October\u20131 November 2020"},{"key":"ref_104","first-page":"1678","article-title":"What Matters in Learning from Offline Human Demonstrations for Robot Manipulation","volume":"Volume 164","author":"Mandlekar","year":"2022","journal-title":"Proceedings of the 5th Conference on Robot Learning, London, UK, 8\u201311 November 2022"},{"key":"ref_105","first-page":"1723","article-title":"BridgeData V2: A Dataset for Robot Learning at Scale","volume":"Volume 229","author":"Walke","year":"2023","journal-title":"Proceedings of the 7th Conference on Robot Learning, Atlanta, GA, USA, 6\u20139 November 2023"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Khazatsky, A., Pertsch, K., Nair, S., Balakrishna, A., Dasari, S., Karamcheti, S., Nasiriany, S., Srirama, M.K., Chen, L.Y., and Ellis, K. (2024, January 15\u201319). DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset. Proceedings of the Robotics: Science and Systems XX, Delft, The Netherlands.","DOI":"10.15607\/RSS.2024.XX.120"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Wang, S., Caesar, H., Nan, L., and Kooij, J.F.P. (2024, January 2\u20135). UniBEV: Multi-modal 3D Object Detection with Uniform BEV Encoders for Robustness against Missing Sensor Modalities. Proceedings of the 2024 IEEE Intelligent Vehicles Symposium (IV), Jeju Island, Republic of Korea.","DOI":"10.1109\/IV55156.2024.10588783"},{"key":"ref_108","unstructured":"Zhang, Y., Gong, S., Xiong, K., Ye, X., Tan, X., Wang, F., Huang, J., Wu, H., and Wang, H. (2024). BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent Space. arXiv."},{"key":"ref_109","unstructured":"Peper, J., Mao, Z., Geng, Y., Pan, S., and Ruchkin, I. (2025). Four Principles for Physically Interpretable World Models. arXiv."},{"key":"ref_110","unstructured":"Huang, X., Xu, Z., Wu, H., Wang, J., Xia, Q., Xia, Y., Li, J., Gao, K., Wen, C., and Wang, C. (March, January 25). L4DR: LiDAR-4DRadar fusion for weather-robust 3D object detection. Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_111","first-page":"50","article-title":"Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/TIV.2023.3307157","article-title":"Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review","volume":"9","author":"Yao","year":"2024","journal-title":"IEEE Trans. Intell. Veh."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5944\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:30:21Z","timestamp":1790073021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5944"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,19]]},"references-count":112,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185944"],"URL":"https:\/\/doi.org\/10.3390\/s26185944","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,19]]}}}