{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T15:46:19Z","timestamp":1784389579135,"version":"3.55.0"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176138"],"award-info":[{"award-number":["62176138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2058"],"award-info":[{"award-number":["U22A2058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176136"],"award-info":[{"award-number":["62176136"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62406175"],"award-info":[{"award-number":["62406175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["2018YFB1305300"],"award-info":[{"award-number":["2018YFB1305300"]}]},{"name":"Shandong Provincial Key Research and Development Program","award":["2019JZZY010130"],"award-info":[{"award-number":["2019JZZY010130"]}]},{"name":"Shandong Provincial Key Research and Development Program","award":["2020CXGC010207"],"award-info":[{"award-number":["2020CXGC010207"]}]},{"name":"Shandong Provincial Key Research and Development Program","award":["tsqn202408016"],"award-info":[{"award-number":["tsqn202408016"]}]},{"name":"Shandong Outstanding Youth Funding","award":["ZR2023YQ054"],"award-info":[{"award-number":["ZR2023YQ054"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Robot."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tro.2025.3547272","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T14:03:11Z","timestamp":1741183391000},"page":"2086-2104","source":"Crossref","is-referenced-by-count":8,"title":["Hierarchical Diffusion Policy: Manipulation Trajectory Generation via Contact Guidance"],"prefix":"10.1109","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7956-6423","authenticated-orcid":false,"given":"Dexin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Ji'nan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5516-2486","authenticated-orcid":false,"given":"Chunsheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Ji'nan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1276-2267","authenticated-orcid":false,"given":"Faliang","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Ji'nan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2605-3818","authenticated-orcid":false,"given":"Yichen","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Ji'nan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.03.003"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3213246"},{"key":"ref4","first-page":"1","article-title":"Denoising diffusion implicit models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song","year":"2021"},{"key":"ref5","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nichol","year":"2021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref7","first-page":"1","article-title":"Diffusion policies as an expressive policy class for offline reinforcement learning","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Wang","year":"2023"},{"key":"ref8","first-page":"9902","article-title":"Planning with diffusion for flexible behavior synthesis","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Janner","year":"2022"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2023.XIX.026"},{"key":"ref10","first-page":"2195","article-title":"Waypoint-based imitation learning for robotic manipulation","volume-title":"Proc. 7th Conf. Robot Learn.","volume":"229","author":"Shi","year":"2023"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3583136"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-control-061623-094742"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00709-2"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-09997-9"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3250269"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9981862"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3333699"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2023.102657"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2022.3161993"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2024.3353075"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3146589"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3059912"},{"key":"ref23","first-page":"2095","article-title":"Residual skill policies: Learning an adaptable skill-based action space for reinforcement learning for robotics","volume-title":"Proc. Conf. Robot Learn.","author":"Rana","year":"2023"},{"key":"ref24","first-page":"22304","article-title":"Compositional foundation models for hierarchical planning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Ajay","year":"2023"},{"key":"ref25","first-page":"21768","article-title":"Chain-of-thought predictive control","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","volume":"235","author":"Jia","year":"2024"},{"key":"ref26","first-page":"287","article-title":"Do as i can, not as i say: Grounding language in robotic affordances","volume-title":"Proc. Conf. robot Learn.","author":"Brohan","year":"2023"},{"key":"ref27","first-page":"26091","article-title":"Deep hierarchical planning from pixels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Hafner","year":"2022"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3192418"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3222996"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01712"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3412630"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8461249"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2956365"},{"key":"ref34","first-page":"18284","article-title":"The magical benchmark for robust imitation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Toyer","year":"2020"},{"key":"ref35","first-page":"726","article-title":"Transporter networks: Rearranging the visual world for robotic manipulation","volume-title":"Proc. Conf. Robot Learn.","author":"Zeng","year":"2021"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9981402"},{"key":"ref37","first-page":"1678","article-title":"What matters in learning from offline human demonstrations for robot manipulation","volume-title":"Proc. Conf. Robot Learn.","author":"Mandlekar","year":"2022"},{"key":"ref38","first-page":"158","article-title":"Implicit behavioral cloning","volume-title":"Proc. Conf. Robot Learn.","author":"Florence","year":"2022"},{"key":"ref39","first-page":"2837","article-title":"Improved contrastive divergence training of energy-based models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Du","year":"2021"},{"key":"ref40","first-page":"3732","article-title":"Learning the stein discrepancy for training and evaluating energy-based models without sampling","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Grathwohl","year":"2020"},{"key":"ref41","first-page":"2730","article-title":"Offline reinforcement learning with realizability and single-policy concentrability","volume-title":"Proc. Conf. Learn. Theory","author":"Zhan","year":"2022"},{"key":"ref42","first-page":"3852","article-title":"Adversarially trained actor critic for offline reinforcement learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cheng","year":"2022"},{"key":"ref43","first-page":"1348","article-title":"Action-quantized offline reinforcement learning for robotic skill learning","volume-title":"Proc. Conf. Robot Learn.","author":"Luo","year":"2023"},{"key":"ref44","first-page":"2052","article-title":"Off-policy deep reinforcement learning without exploration","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Fujimoto","year":"2019"},{"key":"ref45","article-title":"Awac: Accelerating online reinforcement learning with offline datasets","author":"Nair","year":"2020"},{"key":"ref46","first-page":"759","article-title":"Eligibility traces for off-policy policy evaluation","volume-title":"Proc. 17th Int. Conf. Mach. Learn.","author":"Precup","year":"2000"},{"key":"ref47","first-page":"1","article-title":"Gendice: Generalized offline estimation of stationary values","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","year":"2020"},{"key":"ref48","first-page":"1861","article-title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Haarnoja","year":"2018"},{"key":"ref49","first-page":"1179","article-title":"Conservative q-learning for offline reinforcement learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Kumar","year":"2020"},{"key":"ref50","first-page":"104","article-title":"An optimistic perspective on offline reinforcement learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Agarwal","year":"2020"},{"issue":"47","key":"ref51","first-page":"1","article-title":"Cascaded diffusion models for high fidelity image generation","volume":"23","author":"Ho","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref52","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Dhariwal","year":"2021"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00701"},{"key":"ref54","article-title":"Sora: A. review on background, technology, limitations, and opportunities of large vision models","author":"Liu","year":"2024"},{"key":"ref55","first-page":"10021","article-title":"Lion: Latent point diffusion models for 3D shape generation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Vahdat","year":"2022"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00286"},{"key":"ref57","first-page":"1","article-title":"Training diffusion models with reinforcement learning","volume-title":"Proc. ICML 2023 Workshop Many Facets Preference-Based Learn.","author":"Black","year":"2023"},{"key":"ref58","first-page":"1","article-title":"Is conditional generative modeling all you need for decision making?","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Ajay","year":"2023"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA57147.2024.10610175"},{"key":"ref60","first-page":"1","article-title":"Classifier-free diffusion guidance","volume-title":"Proc. NeurIPS 2021 Workshop Deep Generative Models Downstream Appl.","author":"Ho","year":"2021"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.1988.194354"},{"key":"ref62","first-page":"652","article-title":"Pointnet: Deep learning on point sets for 3D classification and segmentation","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Qi","year":"2017"},{"key":"ref63","first-page":"5105","article-title":"PointNet++ : Deep hierarchical feature learning on point sets in a metric space","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Qi","year":"2017"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2024.XX.067"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3363530"},{"key":"ref66","first-page":"22955","article-title":"Behavior transformers: Cloning $ k$ modes with one stone","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Shafiullah","year":"2022"},{"key":"ref67","first-page":"540","article-title":"VoxPoser: Composable 3D value maps for robotic manipulation with language models","volume-title":"Proc. Conf. Robot Learn.","author":"Huang","year":"2023"},{"key":"ref68","first-page":"8469","article-title":"Palm-e: An embodied multimodal language model","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Driess","year":"2023"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3410155"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-023-06303-2"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00589"}],"container-title":["IEEE Transactions on Robotics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8860\/10778592\/10912754.pdf?arnumber=10912754","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T23:26:11Z","timestamp":1768951571000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10912754\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":71,"URL":"https:\/\/doi.org\/10.1109\/tro.2025.3547272","relation":{},"ISSN":["1552-3098","1941-0468"],"issn-type":[{"value":"1552-3098","type":"print"},{"value":"1941-0468","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}