Visual Closed-Loop Control for Pouring Liquids

Connor Schenck, Dieter Fox

Abstract:

Pouring a specific amount of liquid is a challenging
task. In this paper we develop methods for robots to use visual
feedback to perform closed-loop control for pouring liquids. We
propose both a model-based and a model-free method utilizing
deep learning for estimating the volume of liquid in a container.
Our results show that the model-free method is better able
to estimate the volume. We combine this with a simple PID
controller to pour specific amounts of liquid, and show that the
robot is able to achieve an average 38ml deviation from the
target amount. To our knowledge, this is the first use of raw
visual feedback to pour liquids in robotics.