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However, modeling the uncertainty of the dynamics typically results in intractable belief dynamics, making data-efficient planning under uncertainty difficult. This article focuses on the problem of efficiently generating robust open-loop pushing plans. First, we investigate how the belief over object configurations propagates through quasi-static contact dynamics. We exploit the simplified dynamics to predict the variance of the object configuration without sampling from a perturbation distribution. In a sampling-based trajectory optimization algorithm, the gain of the variance is constrained in order to enforce robustness of the plan. Second, we propose an informed trajectory sampling mechanism for drawing robot trajectories that are likely to make contact with the object. This sampling mechanism is shown to significantly improve chances of finding robust solutions, especially when making-and-breaking contacts is required. We demonstrate that the proposed approach is able to synthesize bi-manual pushing trajectories, resulting in successful long-horizon pushing maneuvers without exteroceptive feedback such as vision or tactile feedback. We furthermore deploy the proposed approach in a model-predictive control scheme, demonstrating additional robustness against unmodeled perturbations.<\/jats:p>","DOI":"10.1177\/02783649251318046","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T06:11:46Z","timestamp":1740550306000},"page":"1959-1980","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust pushing: Exploiting quasi-static belief dynamics and contact-informed optimization"],"prefix":"10.1177","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0890-1965","authenticated-orcid":false,"given":"Julius","family":"Jankowski","sequence":"first","affiliation":[{"name":"Robot Learning and Interaction Group, Idiap Research Institute,"},{"name":"Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne"}]},{"given":"Lara","family":"Bruderm\u00fcller","sequence":"additional","affiliation":[{"name":"University of Oxford"}]},{"given":"Nick","family":"Hawes","sequence":"additional","affiliation":[{"name":"University of Oxford"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9036-6799","authenticated-orcid":false,"given":"Sylvain","family":"Calinon","sequence":"additional","affiliation":[{"name":"Robot Learning and Interaction Group, Idiap Research Institute,"},{"name":"Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne"}]}],"member":"179","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"e_1_3_5_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364919887447"},{"key":"e_1_3_5_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/78.978374"},{"key":"e_1_3_5_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9811957"},{"key":"e_1_3_5_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2021.3120931"},{"key":"e_1_3_5_6_1","first-page":"750","volume-title":"Conference on Robot Learning (CoRL)","author":"Bhardwaj M","year":"2022","unstructured":"Bhardwaj M, Sundaralingam B, Mousavian A, et al. 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