Perceiving and Reasoning About Liquids Using Fully Convolutional Networks

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, the robots ask the questions What in my sensory data stream is liquid? and How can I use that to infer all the potential places 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 our networks are able to perceive and reason about liquids, and that integrating temporal information is important to performing these tasks well.