{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:49:45Z","timestamp":1785919785054,"version":"3.56.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032314376","type":"print"},{"value":"9783032314383","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-31438-3_23","type":"book-chapter","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:20:40Z","timestamp":1785918040000},"page":"341-355","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ACE-Grasp: Aleatoric Ambiguity Modeling via\u00a0Consistency and\u00a0Exploration for\u00a0Grasping"],"prefix":"10.1007","author":[{"given":"Yiming","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianghua","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Morrison, D., Corke, P., Leitner, J.: Closing the loop for robotic grasping: a real-time, generative grasp synthesis approach. arXiv 2018. arXiv preprint arXiv:1804.05172","DOI":"10.15607\/RSS.2018.XIV.021"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Kumra, S., Joshi, S., Sahin, F.: Antipodal robotic grasping usreging generative residual convolutional neural network. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9626\u20139633. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9340777"},{"issue":"13\u201314","key":"23_CR3","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1177\/0278364917735594","volume":"36","author":"A Ten Pas","year":"2017","unstructured":"Ten Pas, A., Gualtieri, M., Saenko, K., Platt, R.: Grasp pose detection in point clouds. Int. J. Robot. Res. 36(13\u201314), 1455\u20131473 (2017)","journal-title":"Int. J. Robot. Res."},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Liang, H., et al.: Pointnetgpd: detecting grasp configurations from point sets. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 3629\u20133635. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8794435"},{"key":"23_CR5","unstructured":"Qin, Y., Chen, R., Zhu, H., Song, M., Xu, J., Su, H.: . S4G: amodal single-view single-shot se (3) grasp detection in cluttered scenes. In Conference on Robot Learning, pp. 53\u201365. PMLR (2020)"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Fang, H. S., Wang, C., Gou, M., Lu, C.: Graspnet-1billion: a large-scale benchmark for general object grasping. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11444\u201311453 (2020)","DOI":"10.1109\/CVPR42600.2020.01146"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Gou, M., Fang, H. S., Zhu, Z., Xu, S., Wang, C., Lu, C.: RGB matters: learning 7-dof grasp poses on monocular rgbd images. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 13459\u201313466. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561409"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Lin, Y., Xu, R., Vela, P.: Keypoint-graspnet: keypoint-based 6-dof grasp generation from the monocular rgb-d input (2022). arXiv preprint arXiv:2209.08752","DOI":"10.1109\/ICRA48891.2023.10161284"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Dai, Q., Zhu, Y., Geng, Y., Ruan, C., Zhang, J., Wang, H.: Graspnerf: multiview-based 6-dof grasp detection for transparent and specular objects using generalizable nerf (2022). arXiv preprint arXiv:2210.06575","DOI":"10.1109\/ICRA48891.2023.10160842"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Wen, B., Lian, W., Bekris, K., Schaal, S.: Catgrasp: learning category-level task-relevant grasping in clutter from simulation. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 6401\u20136408. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9811568"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Mahler, J., Matl, M., Liu, X., Li, A., Gealy, D., Goldberg, K.: Dex-Net 3.0: computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 5620\u20135627. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8460887"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Wang, C., Fang, H.S., Gou, M., Fang, H., Gao, J., Lu, C.: Graspness discovery in clutters for fast and accurate grasp detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15964\u201315973 (2021)","DOI":"10.1109\/ICCV48922.2021.01566"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Fang, H.S., et al.: Anygrasp: robust and efficient grasp perception in spatial and temporal domains. IEEE Trans. Robot. 39(5), 3929\u20133945 (2023)","DOI":"10.1109\/TRO.2023.3281153"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Chen, S., Tang, W., Xie, P., Yang, W., Wang, G.: Efficient heatmap-guided 6-dof grasp detection in cluttered scenes. arXiv preprint arXiv:2403.18546 (2024)","DOI":"10.1109\/LRA.2023.3290513"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Mousavian, A., Eppner, C., Fox, D.: 6-dof graspnet: variational grasp generation for object manipulation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2901\u20132910 (2019)","DOI":"10.1109\/ICCV.2019.00299"},{"key":"23_CR16","unstructured":"Murali, A., et al.: Graspgen: a diffusion-based framework for 6-dof grasping with on-generator training. arXiv preprint arXiv:2507.13097 (2025)"},{"key":"23_CR17","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"23_CR18","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"23_CR19","unstructured":"Ma, X., Qin, C., You, H., Ran, H., Fu, Y.: Rethinking network design and local geometry in point cloud: a simple residual MLP framework. arXiv preprint arXiv:2202.07123 (2022)"},{"issue":"12","key":"23_CR20","doi-asserted-by":"publisher","first-page":"4673","DOI":"10.1109\/TCSVT.2021.3100134","volume":"31","author":"P Zhang","year":"2021","unstructured":"Zhang, P., Wang, X., Ma, L., Wang, S., Kwong, S., Jiang, J.: Progressive point cloud upsampling via differentiable rendering. IEEE Trans. Circuits Syst. Video Technol. 31(12), 4673\u20134685 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Qian, G., et al.: Pointnext: revisiting pointnet++ with improved training and scaling strategies. Adv. Neural Inf. Process. Syst. 35, 23192\u201323204 (2022)","DOI":"10.52202\/068431-1685"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Lai, X., et al.: Stratified transformer for 3D point cloud segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8500\u20138509 (2022)","DOI":"10.1109\/CVPR52688.2022.00831"},{"key":"23_CR23","unstructured":"Ma, H., Huang, D.: Towards scale balanced 6-dof grasp detection in cluttered scenes. In: Conference on Robot Learning, pp. 2004\u20132013. PMLR (2023)"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., Mousavian, A., Triebel, R., Fox, D.: Contact-graspnet: efficient 6-dof grasp generation in cluttered scenes. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 13438\u201313444. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561877"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, B., Zhang, H., Lan, X., Wang, H., Tian, Z., Zheng, N.: . Regnet: region-based grasp network for end-to-end grasp detection in point clouds. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 13474\u201313480. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561920"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Wong, A., Wu, Y., Abbasi, S., Nair, S., Chen, Y., Shafiee, M.J.: Fast GraspNeXt: a fast self-attention neural network architecture for multi-task learning in computer vision tasks for robotic grasping on the edge. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2293\u20132297 (2023)","DOI":"10.1109\/CVPRW59228.2023.00224"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Zurbr\u00fcgg, R., et al.: ICGNet: a unified approach for instance-centric grasping. In: 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 4140\u20134146. IEEE (2024)","DOI":"10.1109\/ICRA57147.2024.10611725"},{"key":"23_CR28","unstructured":"Kinga, D., Adam, J.B.: A method for stochastic optimization. In: International Conference on Learning Representations (ICLR), vol. 5, no. 6 (2015)"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Morrison, D., Corke, P., Leitner, J.: Closing the loop for robotic grasping: a real-time, generative grasp synthesis approach. arXiv preprint arXiv:1804.05172 (2018)","DOI":"10.15607\/RSS.2018.XIV.021"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Liang, H., et al.: Pointnetgpd: detecting grasp configurations from point sets. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 3629\u20133635. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8794435"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, Z., Xie, S., Zheng, W.S.: Transgrasp: a multi-scale hierarchical point transformer for 7-dof grasp detection. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 1533\u20131539. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9812001"},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., Savarese, S.: 4D spatio-temporal convnets: minkowski convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3075\u20133084 (2019)","DOI":"10.1109\/CVPR.2019.00319"},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR34","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. MICCAI 2015. LNCS, vol. 9351, pp. 234-241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-31438-3_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:20:53Z","timestamp":1785918053000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-31438-3_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,6]]},"ISBN":["9783032314376","9783032314383"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-31438-3_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,6]]},"assertion":[{"value":"6 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lyon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"17 August 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}