Accurate Pouring using Model Predictive Control Enabled by Recurrent Neural Network Humans perform the task of pouring often and in which exhibit consistent accuracy regardless of the complicated dynamics of the liquid. Model predictive control (MPC) appears to be a natural candidate solution for the task of accurate pouring considering its wide use in industrial applications. However, MPC requires the model of the system in question. Since an accurate model of the liquid dynamics is difficult to obtain, the usefulness of MPC for the pouring task is uncertain. In this work, we model the dynamics of water using a recurrent neural network (RNN), which enables the use of MPC for pouring control. We evaluated our RNNenabled MPC controller using a physical system we made ourselves and averaged a pouring error of 16. 4mL over 5 different source containers. We also compared our controller with a baseline switch controller and showed that our controller achieved a much higher accuracy than the baseline controller.

Realtime Liquid Pouring Motion Generation: EndtoEnd Sensorimotor Coordination for Unknown Liquid Dynamics Trained with Deep Neural Networks We propose a sensorimotor dynamical system model for pouring unknown liquids. With our system, a robot holds and shakes a bottle to estimate the characteristics of the contained liquid, such as viscosity and fill level, without calculating to determine their parameters. Next, the robot pours a specified amount of the liquid into another container. The system needs to integrate information on the robotxe2x80x99s actions, the liquids, the container, and the surrounding environment to perform the estimation and execute a continuous pouring motion using the same model. We use deep neural networks (DNN) to construct the system. The DNN model repeats prediction and execution of the actions to be taken in the next time step based on the input sensorimotor data, including camera images, force sensor data, and joint angles. At the same time, the DNN model acquires liquid characteristics in the internal state. We confirmed that the DNN model can control the robot to pour a desired amount of liquid with unknown viscosity and fill level.