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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 RNN-enabled MPC controller using a physical system we made ourselves and averaged a pouring error of 16.4 mL 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. 

Robots that succeed in factories may struggle to complete even the simplest daily task that humans take for granted, because the change of environment makes the task exceedingly difficult. Aiming to teach robots to perform daily interactive manipulation in a changing environment using human demonstrations, we collected our own data of interactive manipulation. The dataset focuses on the position, orientation, force, and torque of objects manipulated in daily tasks. The dataset includes 1,603 trials of 32 types of daily motions and 1,596 trials of pouring alone, as well as helper code. We present our dataset to facilitate the research on task-oriented interactive manipulation.