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Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions., , , , , and . IROS, page 5351-5357. IEEE, (2020)Inverse Design for Fluid-Structure Interactions using Graph Network Simulators., , , , , , and . NeurIPS, (2022)Graph network simulators can learn discontinuous, rigid contact dynamics., , , , , , and . CoRL, volume 205 of Proceedings of Machine Learning Research, page 1157-1167. PMLR, (2022)Learning 3D Particle-based Simulators from RGB-D Videos., , , , , , and . ICLR, OpenReview.net, (2024)Learning rigid dynamics with face interaction graph networks., , , , , , and . ICLR, OpenReview.net, (2023)Learning rigid-body simulators over implicit shapes for large-scale scenes and vision., , , , , and . CoRR, (2024)Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation., , , , and . CoRL, volume 78 of Proceedings of Machine Learning Research, page 77-86. PMLR, (2017)To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions., , , , , and . CoRR, (2019)IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates., , , and . CoRR, (2020)