  Robust and accurate estimation of liquid height lies as an essential part of
pouring tasks for service robots. However, vision-based methods often fail in
occluded conditions while audio-based methods cannot work well in a noisy
environment. We instead propose a multimodal pouring network (MP-Net) that is
able to robustly predict liquid height by conditioning on both audition and
haptics input. MP-Net is trained on a self-collected multimodal pouring
dataset. This dataset contains 300 robot pouring recordings with audio and
force/torque measurements for three types of target containers. We also augment
the audio data by inserting robot noise. We evaluated MP-Net on our collected
dataset and a wide variety of robot experiments. Both network training results
and robot experiments demonstrate that MP-Net is robust against noise and
changes to the task and environment. Moreover, we further combine the predicted
height and force data to estimate the shape of the target container.
