{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T15:30:41Z","timestamp":1769182241998,"version":"3.49.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T00:00:00Z","timestamp":1653091200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T00:00:00Z","timestamp":1653091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Intel Serv Robotics"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11370-022-00425-7","type":"journal-article","created":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T09:02:33Z","timestamp":1653123753000},"page":"335-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A few-shot learning framework for planar pushing of unknown objects"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9948-7960","authenticated-orcid":false,"given":"Ziyan","family":"Gao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0661-9536","authenticated-orcid":false,"given":"Armagan","family":"Elibol","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5736-0769","authenticated-orcid":false,"given":"Nak Young","family":"Chong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,21]]},"reference":[{"key":"425_CR1","unstructured":"Agrawal P, Nair AV, Abbeel P, Malik J, Levine S (2016) Learning to poke by poking: experiential learning of intuitive physics. In: Advances in neural information processing systems, pp 5074\u20135082"},{"key":"425_CR2","doi-asserted-by":"crossref","unstructured":"Allevato A, Pryor M, Thomaz A (2020) Multi-parameter real-world system identification using iterative residual tuning. In: Proceedings of the ASME international design and technical conference. St. Louis","DOI":"10.1115\/1.0001838V"},{"key":"425_CR3","unstructured":"Allevato A, Short ES, Pryor M, Thomaz A (2020) Tunenet: one-shot residual tuning for system identification and sim-to-real robot task transfer. In: LP Kaelbling, D Kragic, K Sugiura (eds) Proceedings of the conference on robot learning, Proceedings of machine learning research, vol 100. PMLR, pp 445\u2013455. http:\/\/proceedings.mlr.press\/v100\/allevato20a.html"},{"key":"425_CR4","doi-asserted-by":"publisher","unstructured":"Arruda E, Mathew MJ, Kopicki M, Mistry M, Azad M, Wyatt JL (2017) Uncertainty averse pushing with model predictive path integral control. In: IEEE-RAS international conference on humanoid robots, pp 497\u2013502. https:\/\/doi.org\/10.1109\/HUMANOIDS.2017.8246918","DOI":"10.1109\/HUMANOIDS.2017.8246918"},{"key":"425_CR5","doi-asserted-by":"crossref","unstructured":"Bauza M, Alet F, Lin YC, Lozano-P\u00e9rez T, Kaelbling LP, Isola P, Rodriguez A (2019) Omnipush: accurate, diverse, real-world dataset of pushing dynamics with rgb-d video. arXiv preprint arXiv:1910.00618","DOI":"10.1109\/IROS40897.2019.8967920"},{"key":"425_CR6","doi-asserted-by":"publisher","unstructured":"Byravan A, Fox D (2017) SE3-nets: learning rigid body motion using deep neural networks. In: Proceedings\u2014IEEE international conference on robotics and automation, vol 3, pp 173\u2013180. https:\/\/doi.org\/10.1109\/ICRA.2017.7989023","DOI":"10.1109\/ICRA.2017.7989023"},{"key":"425_CR7","doi-asserted-by":"publisher","unstructured":"Chang L, Smith J, Fox D (2012) Interactive singulation of objects from a pile. In: Proceedings\u2014IEEE international conference on robotics and automation, pp 3875\u20133882. https:\/\/doi.org\/10.1109\/ICRA.2012.6224575","DOI":"10.1109\/ICRA.2012.6224575"},{"key":"425_CR8","unstructured":"CM Labs Vortex Studio Academic. https:\/\/www.cm-labs.com\/vortex-studio\/software\/vortex-studio-academic-access\/. Accessed 30 Sept 2020"},{"key":"425_CR9","doi-asserted-by":"publisher","unstructured":"Cosgun A, Hermans T, Emeli V, Stilman M (2011) Push planning for object placement on cluttered table surfaces. In: 2011 IEEE\/RSJ international conference on intelligent robots and systems, pp 4627\u20134632. https:\/\/doi.org\/10.1109\/IROS.2011.6094737","DOI":"10.1109\/IROS.2011.6094737"},{"key":"425_CR10","doi-asserted-by":"publisher","unstructured":"Danielczuk M, Mahler J, Correa C, Goldberg K (2018) Linear push policies to increase grasp access for robot bin picking. In: 2018 IEEE 14th international conference on automation science and engineering (CASE), pp 1249\u20131256. https:\/\/doi.org\/10.1109\/COASE.2018.8560406","DOI":"10.1109\/COASE.2018.8560406"},{"key":"425_CR11","doi-asserted-by":"publisher","unstructured":"Depierre A, Dellandr\u00e9a E, Chen L (2018) Jacquard: a large scale dataset for robotic grasp detection. In: 2018 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 3511\u20133516. https:\/\/doi.org\/10.1109\/IROS.2018.8593950","DOI":"10.1109\/IROS.2018.8593950"},{"key":"425_CR12","doi-asserted-by":"crossref","unstructured":"Dogar M, Srinivasa S (2011) A framework for push-grasping in clutter. Robot Sci Syst VII 1","DOI":"10.15607\/RSS.2011.VII.009"},{"key":"425_CR13","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(7)"},{"key":"425_CR14","unstructured":"Ebert F, Finn C, Dasari S, Xie A, Lee A, Levine S (2018) Visual foresight: model-based deep reinforcement learning for vision-based robotic control. arXiv preprint arXiv:1812.00568"},{"key":"425_CR15","unstructured":"Ebert F, Finn C, Lee AX, Levine S (2017) Self-supervised visual planning with temporal skip connections. In: 1st annual conference on robot learning, CoRL 2017, Mountain View, California, USA, Nov 13\u201315, 2017, Proceedings, Proceedings of machine learning research, vol 78. PMLR, pp 344\u2013356. http:\/\/proceedings.mlr.press\/v78\/frederik-ebert17a.html"},{"key":"425_CR16","unstructured":"Ebert F, Finn C, Lee AX, Levine S (2017) Self-supervised visual planning with temporal skip connections. In: CoRL, pp 344\u2013356"},{"key":"425_CR17","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/978-3-030-28619-4_32","volume":"10","author":"A Eitel","year":"2020","unstructured":"Eitel A, Hauff N, Burgard W (2020) Learning to singulate objects using a push proposal network. Springer Proc Adv Robot 10:405\u2013419. https:\/\/doi.org\/10.1007\/978-3-030-28619-4_32","journal-title":"Springer Proc Adv Robot"},{"key":"425_CR18","unstructured":"Finn C, Goodfellow I, Levine S (2016) Unsupervised learning for physical interaction through video prediction. arXiv preprint arXiv:1605.07157"},{"key":"425_CR19","doi-asserted-by":"publisher","unstructured":"Finn C, Levine S (2017) Deep visual foresight for planning robot motion. In: Proceedings\u2014IEEE international conference on robotics and automation, pp 2786\u20132793. https:\/\/doi.org\/10.1109\/ICRA.2017.7989324","DOI":"10.1109\/ICRA.2017.7989324"},{"key":"425_CR20","doi-asserted-by":"crossref","unstructured":"Florence P, Manuelli L, Tedrake R (2019) Self-supervised correspondence in visuomotor policy learning","DOI":"10.1109\/LRA.2019.2956365"},{"key":"425_CR21","unstructured":"Fragkiadaki K, Agrawal P, Levine S, Malik J (2016) Learning visual predictive models of physics for playing billiards. In: 4th international conference on learning representations, ICLR 2016\u2014conference track proceedings, pp 1\u201312"},{"key":"425_CR22","doi-asserted-by":"publisher","unstructured":"Gao Z, Elibol A, Chong NY (2020) A 2-stage framework for learning to push unknown objects. In: Joint IEEE international conference on development and learning and epigenetic robotics, pp 1\u20137. https:\/\/doi.org\/10.1109\/ICDL-EpiRob48136.2020.9278075","DOI":"10.1109\/ICDL-EpiRob48136.2020.9278075"},{"key":"425_CR23","doi-asserted-by":"crossref","unstructured":"Gao Z, Elibol A, Chong NY (2020) Non-prehensile manipulation learning through self-supervision. In: IEEE international conference on robotic computing, pp 93\u201399","DOI":"10.1109\/IRC.2020.00022"},{"key":"425_CR24","doi-asserted-by":"publisher","unstructured":"Gao Z, Elibol A, Chong NY (2021) Planar pushing of unknown objects using a large-scale simulation dataset and few-shot learning. In: 2021 IEEE 17th international conference on automation science and engineering (CASE), pp 341\u2013347. https:\/\/doi.org\/10.1109\/CASE49439.2021.9551513","DOI":"10.1109\/CASE49439.2021.9551513"},{"key":"425_CR25","unstructured":"Goo W, Niekum S (2020) Local nonparametric meta-learning. arXiv preprint arXiv:2002.03272"},{"key":"425_CR26","doi-asserted-by":"crossref","unstructured":"Goyal S, Ruina A, Papadopoulos J.: Planar sliding with dry friction part 1. limit surface and moment function. Wear 143(2), 307\u2013330 (1991)","DOI":"10.1016\/0043-1648(91)90104-3"},{"key":"425_CR27","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"425_CR28","doi-asserted-by":"crossref","unstructured":"Hermans T, Li F, Rehg, JM, Bobick AF (2013) Learning contact locations for pushing and orienting unknown objects. In: IEEE-RAS international conference on humanoid robots, pp 435\u2013442","DOI":"10.1109\/HUMANOIDS.2013.7030011"},{"key":"425_CR29","unstructured":"Kim H, Mnih A, Schwarz J, Garnelo M, Eslami A, Rosenbaum D, Vinyals O, Teh YW (2019) Attentive neural processes. arXiv preprint arXiv:1901.05761"},{"key":"425_CR30","doi-asserted-by":"crossref","unstructured":"Kloss A, Bauza M, Wu J, Tenenbaum JB, Rodriguez A, Bohg J (2020) Accurate vision-based manipulation through contact reasoning. In: IEEE international conference on robotics and automation, pp 6738\u20136744","DOI":"10.1109\/ICRA40945.2020.9197409"},{"key":"425_CR31","unstructured":"Kloss A, Schaal S, Bohg J (2018) Combining learned and analytical models for predicting action effects from sensory data. Int J Robot Res 0278364920954896"},{"key":"425_CR32","unstructured":"Kumar KN, Essa I, Ha S, Liu CK (2019) Estimating mass distribution of articulated objects using non-prehensile manipulation. arXiv preprint arXiv:1907.03964"},{"issue":"4\u20135","key":"425_CR33","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1177\/0278364917710318","volume":"37","author":"S Levine","year":"2018","unstructured":"Levine S, Pastor P, Krizhevsky A, Ibarz J, Quillen D (2018) Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int J Robot Res 37(4\u20135):421\u2013436. https:\/\/doi.org\/10.1177\/0278364917710318","journal-title":"Int J Robot Res"},{"key":"425_CR34","doi-asserted-by":"publisher","unstructured":"Li JK, Lee WS, Hsu D (2018) Push-net: deep planar pushing for objects with unknown physical properties. In: H Kress-Gazit, SS Srinivasa, T Howard, N Atanasov (eds) Robotics: science and systems XIV. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA, June 26\u201330, 2018 . https:\/\/doi.org\/10.15607\/RSS.2018.XIV.024.http:\/\/www.roboticsproceedings.org\/rss14\/p24.html","DOI":"10.15607\/RSS.2018.XIV.024"},{"key":"425_CR35","doi-asserted-by":"publisher","unstructured":"Lin C, Grner M, Ruppel P, Liang H, Hendrich N, Zhang J (2020) Self-adapting recurrent models for object pushing from learning in simulation. In: IEEE international conference on intelligent robots and systems, pp 5304\u20135310. https:\/\/doi.org\/10.1109\/IROS45743.2020.9341076","DOI":"10.1109\/IROS45743.2020.9341076"},{"key":"425_CR36","doi-asserted-by":"crossref","unstructured":"Lynch KM, Maekawa H, Tanie K (1992) Manipulation and active sensing by pushing using tactile feedback. In: IEEE\/RSJ international conference on intelligent robots and systems, pp 416\u2013421","DOI":"10.1109\/IROS.1992.587370"},{"issue":"3","key":"425_CR37","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1177\/027836498600500303","volume":"5","author":"MT Mason","year":"1986","unstructured":"Mason MT (1986) Mechanics and planning of manipulator pushing operations. Int J Robot Res 5(3):53\u201371. https:\/\/doi.org\/10.1177\/027836498600500303","journal-title":"Int J Robot Res"},{"key":"425_CR38","doi-asserted-by":"publisher","unstructured":"Mavrakis N, Ghalamzan EAM, Stolkin R (2020) Estimating an object\u2019s inertial parameters by robotic pushing: a data-driven approach. IEEE international conference on intelligent robots and systems, pp 9537\u20139544. https:\/\/doi.org\/10.1109\/IROS45743.2020.9341112","DOI":"10.1109\/IROS45743.2020.9341112"},{"key":"425_CR39","doi-asserted-by":"publisher","unstructured":"Pinto L, Gupta A (2016) Supersizing self-supervision: learning to grasp from 50K tries and 700 robot hours. In: Proceedings\u2014IEEE international conference on robotics and automation 2016-June, pp 3406\u20133413. https:\/\/doi.org\/10.1109\/ICRA.2016.7487517","DOI":"10.1109\/ICRA.2016.7487517"},{"key":"425_CR40","doi-asserted-by":"crossref","unstructured":"Rohmer E, Singh SPN, Freese M (2013) Coppeliasim (formerly v-rep): a versatile and scalable robot simulation framework. In: IEEE\/RSJ international conference on intelligent robots and systems","DOI":"10.1109\/IROS.2013.6696520"},{"key":"425_CR41","doi-asserted-by":"publisher","unstructured":"Song C, Boularias A (2020) A probabilistic model for planar sliding of objects with unknown material properties: identification and robust planning. In: 2020 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 5311\u20135318. https:\/\/doi.org\/10.1109\/IROS45743.2020.9341468","DOI":"10.1109\/IROS45743.2020.9341468"},{"key":"425_CR42","doi-asserted-by":"crossref","unstructured":"St\u00fcber J, Zito C, Stolkin R (2020) Let\u2019s push things forward: a survey on robot pushing. Front Robot AI 7:8","DOI":"10.3389\/frobt.2020.00008"},{"key":"425_CR43","doi-asserted-by":"crossref","unstructured":"Walker J, Doersch C, Gupta A, Hebert M (2016) An uncertain future: forecasting from static images using variational autoencoders. In: European conference on computer vision. Springer, pp 835\u2013851","DOI":"10.1007\/978-3-319-46478-7_51"},{"key":"425_CR44","doi-asserted-by":"publisher","unstructured":"Wang C, Wang S, Romero B, Veiga F, Adelson E (2020) SwingBot: learning physical features from in-hand tactile exploration for dynamic swing-up manipulation. In: IEEE international conference on intelligent robots and systems, pp 5633\u20135640. https:\/\/doi.org\/10.1109\/IROS45743.2020.9341006","DOI":"10.1109\/IROS45743.2020.9341006"},{"key":"425_CR45","doi-asserted-by":"publisher","first-page":"68277","DOI":"10.1109\/ACCESS.2021.3077117","volume":"9","author":"J Wang","year":"2021","unstructured":"Wang J, Hu C, Wang Y, Zhu Y (2021) Dynamics learning with object-centric interaction networks for robot manipulation. IEEE Access 9:68277\u201368288. https:\/\/doi.org\/10.1109\/ACCESS.2021.3077117","journal-title":"IEEE Access"},{"key":"425_CR46","doi-asserted-by":"publisher","unstructured":"Wu J, Lim JJ, Zhang H, Tenenbaum JB, Freeman WT (2016) Physics 101: learning physical object properties from unlabeled videos. In: British machine vision conference 2016, BMVC 2016, 2016 Sept, pp 39.1\u201339.12. https:\/\/doi.org\/10.5244\/C.30.39","DOI":"10.5244\/C.30.39"},{"key":"425_CR47","unstructured":"Xu Z, He Z, Wu J, Song S (2020) Learning 3d dynamic scene representations for robot manipulation. CoRR. arxiv:2011.01968"},{"key":"425_CR48","doi-asserted-by":"crossref","unstructured":"Xu Z, Wu J, Zeng A, Tenenbaum J.B, Song S (2019) Densephysnet: learning dense physical object representations via multi-step dynamic interactions. In: Robotics: science and systems (RSS). http:\/\/www.zhenjiaxu.com\/DensePhysNet\/","DOI":"10.15607\/RSS.2019.XV.046"},{"key":"425_CR49","doi-asserted-by":"crossref","unstructured":"Xu Z, Yu W, Herzog A, Lu W, Fu C, Tomizuka M, Bai Y, Liu CK, Ho D (2020) Cocoi: contact-aware online context inference for generalizable non-planar pushing. arXiv preprint arXiv:2011.11270","DOI":"10.1109\/IROS51168.2021.9636836"},{"key":"425_CR50","unstructured":"Ye Y, Gandhi D, Gupta A, Tulsiani S (2019) Object-centric forward modeling for model predictive control. arXiv (CoRL), pp 1\u201313"},{"key":"425_CR51","doi-asserted-by":"crossref","unstructured":"Yu KT, Bauza M, Fazeli N, Rodriguez A (2016) More than a million ways to be pushed. a high-fidelity experimental dataset of planar pushing. In: IEEE\/RSJ international conference on intelligent robots and systems, pp 30\u201337","DOI":"10.1109\/IROS.2016.7758091"},{"key":"425_CR52","doi-asserted-by":"crossref","unstructured":"Yu W, Tan J, Liu CK, Turk G (2017) Preparing for the unknown: learning a universal policy with online system identification. Robot Sci Syst. https:\/\/doi.org\/10.15607\/rss.2017.xiii.048","DOI":"10.15607\/RSS.2017.XIII.048"},{"key":"425_CR53","unstructured":"Zeng A, Florence P, Tompson J, Welker S, Chien J, Attarian M, Armstrong T, Krasin I, Duong, D, Sindhwani V, Lee J (2021) Transporter networks: rearranging the visual world for robotic manipulation"}],"container-title":["Intelligent Service Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11370-022-00425-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11370-022-00425-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11370-022-00425-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T08:34:15Z","timestamp":1657701255000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11370-022-00425-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,21]]},"references-count":53,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["425"],"URL":"https:\/\/doi.org\/10.1007\/s11370-022-00425-7","relation":{},"ISSN":["1861-2776","1861-2784"],"issn-type":[{"value":"1861-2776","type":"print"},{"value":"1861-2784","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,21]]},"assertion":[{"value":"10 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}