Perceiving and Reasoning About Liquids Using Fully Convolutional Networks

Connor Schenck, Dieter Fox

Abstract:

Liquids are an important part of many common manipulation tasks in human environments. If we wish to have
robots that can accomplish these types of tasks, they must be able to interact with liquids in an intelligent
manner. In this paper, we investigate ways for robots to perceive and reason about liquids. That is, a robot asks
the questions What in the visual data stream is liquid? and How can I use that to infer all the potential
places where liquid might be? We collected two datasets to evaluate these questions, one using a realistic liquid
simulator and another on our robot. We used fully convolutional neural networks to learn to detect and track
liquids across pouring sequences. Our results show that these networks are able to perceive and reason about
liquids, and that integrating temporal information is important to performing such tasks well.