Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions

Humans use simple probing actions to develop intuition about the physical behaviour of common objects. Such intuition is particularly useful for adaptive estimation of favourable manipulation strategies of those objects in novel contexts. For example, observing the effect of tilt on a transparent bottle containing an unknown liquid provides clues on how the liquid might be poured. It is desirable to equip general-purpose robotic systems with this capability because it is inevitable that they will encounter novel objects and scenarios. In this paper, we teach a robot to use a simple, specified probing strategy – stirring with a stick – to reduce spillage while pouring unknown liquids. In the probing step, we continuously observe the effects of a real robot stirring a liquid, while simultaneously tuning the parameters to a model (simulator) until the two outputs are in agreement. We obtain optimal simulation parameters, characterising the unknown liquid, via a Bayesian Optimiser that minimises the discrepancy between real and simulated outcomes. Then, we optimise the pouring policy conditioning on the optimal simulation parameters determined via stirring. We show that using stirring as a probing strategy results in reduced spillage for three qualitatively different liquids when executed on a UR10 Robot, compared to probing via pouring. Finally, we provide quantitative insights into the reason for stirring being a suitable calibration task for pouring – a step towards automatic discovery of probing strategies.