全畳み込みネットワークによる液体の知覚と推論
Perceiving and Reasoning About Liquids Using Fully Convolutional Networks
ロボットが注ぐ動作の中で液体を検出・追跡するために全畳み込みニューラルネットワークを学習させ、時間情報の統合が重要であることを示した。
著者: Conor Schenck, Dieter Fox
分類: cs.RO, cs.CV
原文アブストラクト
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.
