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Our brains are able to exploit coarse physical models of fluids to solveeveryday manipulation tasks. There has been considerable interest in developingsuch a capability in robots so that they can autonomously manipulate fluidsadapting to different conditions. In this paper, we investigate the problem ofadaptation to liquids with different characteristics. We develop a simplecalibration task (stirring with a stick) that enables rapid inference of theparameters of the liquid from RBG data. We perform the inference in the spaceof simulation parameters rather than on physically accurate parameters. Thisfacilitates prediction and optimization tasks since the inferred parameters maybe fed directly to the simulator. We demonstrate that our "stirring" learnerperforms better than when the robot is calibrated with pouring actions. We showthat our method is able to infer properties of three different liquids --water, glycerin and gel -- and present experimental results by executingstirring and pouring actions on a UR10. We believe that decoupling of thetraining actions from the goal task is an important step towards simple,autonomous learning of the behavior of different fluids in unstructuredenvironments.

To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions