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We propose a deep visuo-tactile model for realtime estimation of the liquidinside a deformable container in a proprioceptive way.We fuse two sensorymodalities, i.e., the raw visual inputs from the RGB camera and the tactilecues from our specific tactile sensor without any extra sensor calibrations.Therobotic system is well controlled and adjusted based on the estimation model inreal time. The main contributions and novelties of our work are listed asfollows: 1) Explore a proprioceptive way for liquid volume estimation bydeveloping an end-to-end predictive model with multi-modal convolutionalnetworks, which achieve a high precision with an error of around 2 ml in theexperimental validation. 2) Propose a multi-task learning architecture whichcomprehensively considers the losses from both classification and regressiontasks, and comparatively evaluate the performance of each variant on thecollected data and actual robotic platform. 3) Utilize the proprioceptiverobotic system to accurately serve and control the requested volume of liquid,which is continuously flowing into a deformable container in real time. 4)Adaptively adjust the grasping plan to achieve more stable grasping andmanipulation according to the real-time liquid volume prediction.

Visual-Tactile Sensing for Real-time Liquid Volume Estimation in Grasping