{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T05:33:13Z","timestamp":1782970393966,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002701","name":"Ministry of Education of the Republic of Korea","doi-asserted-by":"crossref","award":["RS-2025-25399600"],"award-info":[{"award-number":["RS-2025-25399600"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we propose simDP, a sim-to-real transfer framework that enables diffusion policies trained in simulations for the efficient deployment on real-world robots. The key idea is to reduce the sim-to-real gap by aligning the action and observation spaces between simulation and reality. Specifically, we reformulate the action space using end-effector pose and binary gripper state, which can be shared between simulated and physical robots. In addition, we use camera-based visual observations as the primary sensing modality in both domains and train a real-world observation encoder to align with the latent representation learned in simulation. This design allows the action decoder trained in simulation to be reused in the real-world with minimal modification. We evaluated simDP on object manipulation tasks derived from the MimicGen benchmark and show that a simulation-trained diffusion decoder, when combined with a real-world adapted observation encoder, achieves task completion performance similar to and in some cases better than diffusion policies trained only on limited real-world data. These results, obtained across four manipulation tasks in a calibrated real-world transfer setting, suggest that reusing a simulation-trained action decoder with lightweight real-world encoder adaptation provides an effective strategy for controlled sim-to-real transfer, while broader evaluation across diverse tasks, environments, and robot embodiments remains an important direction for future work.<\/jats:p>","DOI":"10.3390\/s26134079","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T05:49:33Z","timestamp":1782712173000},"page":"4079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["simDP: Sim-to-Real Transfer with Shared Action Spaces"],"prefix":"10.3390","volume":"26","author":[{"given":"Chanhyuk","family":"Jung","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Keimyung University, Daegu 42601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongbin","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Robot Engineering, Keimyung University, Daegu 42601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5393-2560","authenticated-orcid":false,"given":"Sungkeun","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Robot Engineering, Keimyung University, Daegu 42601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7284-0768","authenticated-orcid":false,"given":"Byoung Chul","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Keimyung University, Daegu 42601, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"ref_1","unstructured":"Open X-Embodiment Collaboration, O\u2019Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., and Gupta, A. (2023). Open X-Embodiment: Robotic Learning Datasets and RT-X Models. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1177\/02783649241273668","article-title":"Diffusion Policy: Visuomotor Policy Learning via Action Diffusion","volume":"44","author":"Chi","year":"2025","journal-title":"Int. J. Robot. Res."},{"key":"ref_3","unstructured":"Mandlekar, A., Nasiriany, S., Wen, B., Akinola, I., Narang, Y., Fan, L., Zhu, Y., and Fox, D. (2023). MimicGen: A Data Generation System for Scalable Robot Learning Using Human Demonstrations. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Garcia, R., Strudel, R., Chen, S., Arlaud, E., Laptev, I., and Schmid, C. (2023, January 1\u20135). Robust Visual Sim-to-Real Transfer for Robotic Manipulation. Proceedings of the 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA.","DOI":"10.1109\/IROS55552.2023.10342471"},{"key":"ref_5","unstructured":"Lou, H., Zhang, M., Geng, H., Zhou, H., He, S., Gao, Z., Zhao, S., Mao, J., Abbeel, P., and Malik, J. (2026, January 23\u201327). D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping. Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pitkevich, A., and Makarov, I. (2024, January 19\u201321). A Survey on Sim-to-Real Transfer Methods for Robotic Manipulation. Proceedings of the 2024 IEEE 22nd Jubilee International Symposium on Intelligent Systems and Informatics (SISY), Pula, Croatia.","DOI":"10.1109\/SISY62279.2024.10737545"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P. (2017). Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. arXiv.","DOI":"10.1109\/IROS.2017.8202133"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cao, H.G., Zeng, W., and Wu, I.C. (June, January 29). Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), London, UK.","DOI":"10.1109\/ICRA48891.2023.10161477"},{"key":"ref_9","unstructured":"Muratore, F., Eilers, C., Gienger, M., and Peters, J. (2020). Bayesian Domain Randomization for Sim-to-Real Transfer. arXiv."},{"key":"ref_10","unstructured":"James, S., Davison, A.J., and Johns, E. (2017). Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task. Proceedings of the Conference on Robot Learning, PMLR."},{"key":"ref_11","unstructured":"Yuan, Z., Wei, T., Cheng, S., Zhang, G., Chen, Y., and Xu, H. (2024). Learning to manipulate anywhere: A visual generalizable framework for reinforcement learning. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"James, S., Wohlhart, P., Kalakrishnan, M., Kalashnikov, D., Irpan, A., Ibarz, J., Levine, S., Hadsell, R., and Bousmalis, K. (2019, January 16\u201320). Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01291"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qureshi, M.N., and Yip, M.C. (2024). SplatSim: Zero-Shot Sim2Real Transfer of RGB Manipulation Policies Using Gaussian Splatting. arXiv.","DOI":"10.1109\/ICRA55743.2025.11128339"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Reuss, M., Li, M., Xia, Y., and Lioutikov, R. (2023). Goal-Conditioned Imitation Learning Using Score-Based Diffusion Policies. arXiv.","DOI":"10.15607\/RSS.2023.XIX.028"},{"key":"ref_15","unstructured":"Pearce, T., Rashid, T., Kanervisto, A., Gavves, E., Spiers, A., and Lassance, L. (2023). Imitating Short-Horizon Robotic Manipulation with Diffusion. arXiv."},{"key":"ref_16","unstructured":"Ze, Y., Chi, C., and Chen, B. (2024). 3D Diffusion Policy. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wolf, R., Shi, Y., Liu, S., and Rayyes, R. (2025). Diffusion Models for Robotic Manipulation: A Survey. arXiv.","DOI":"10.3389\/frobt.2025.1606247"},{"key":"ref_18","unstructured":"Ahn, D., Jung, C., Baek, J., Yoo, S., and Ko, B.C. (2026, January 1\u20135). Shifted Flow Policy: Uncertainty-Aware Time Reparameterization for Visuomotor Learning. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Vienna, Austria."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ghosh, D., Walke, H., Pertsch, K., Black, K., Mees, O., Dasari, S., Hejna, J., Kreiman, T., Xu, C., and Luo, J. (2024, January 15\u201319). Octo: An Open-Source Generalist Robot Policy. Proceedings of the Robotics: Science and Systems (RSS), Delft, The Netherlands.","DOI":"10.15607\/RSS.2024.XX.090"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., and Hsu, J. (2022). RT-1: Robotics Transformer for Real-World Control at Scale. arXiv.","DOI":"10.15607\/RSS.2023.XIX.025"},{"key":"ref_21","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sun, B., and Saenko, K. (2016). Deep CORAL: Correlation alignment for deep domain adaptation. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Springer.","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref_23","unstructured":"M\u00fcller, M., Dosovitskiy, A., Ghanem, B., and Koltun, V. (2018). Driving policy transfer via modularity and abstraction. Proceedings of the Conference on Robot Learning (CoRL), PMLR."},{"key":"ref_24","unstructured":"Julian, R., Swanson, B., Sukhatme, G.S., Levine, S., Finn, C., and Hausman, K. (2021). Never stop learning: The effectiveness of fine-tuning in robotic reinforcement learning. Proceedings of the Conference on Robot Learning (CoRL), PMLR."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Barnes, C., Lu, J., Yang, J., and Li, H. (2019, January 15\u201320). On the Continuity of Rotation Representations in Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00589"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/TMECH.2025.3586019","article-title":"Flow Policy: Generalizable Visuomotor Policy Learning via Flow Matching","volume":"31","author":"Fang","year":"2026","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhao, T.Z., Kumar, V., Levine, S., and Finn, C. (2023, January 10\u201314). Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware. Proceedings of the Robotics: Science and Systems (RSS), Daegu, Republic of Korea.","DOI":"10.15607\/RSS.2023.XIX.016"},{"key":"ref_30","first-page":"2579","article-title":"Visualizing Data Using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4079\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T04:19:47Z","timestamp":1782965987000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"references-count":30,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["s26134079"],"URL":"https:\/\/doi.org\/10.3390\/s26134079","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]}}}