幻覚的なシーン表現を用いた関係指示に基づく物体配置の学習
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations
単一画像から空間関係ごとの画素単位の物体配置確率を推定するCNNを提案し、幻覚した高レベルシーン表現の分類を補助タスクとして学習することで、正解データや3Dモデルなしで関係指示に沿った物体配置を実現した。
著者: Oier Mees, Alp Emek, Johan Vertens, Wolfram Burgard
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Robots coexisting with humans in their environment and performing services for them need the ability to interact with them. One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user. In this work, we present a convolutional neural network for estimating pixelwise object placement probabilities for a set of spatial relations from a single input image. During training, our network receives the learning signal by classifying hallucinated high-level scene representations as an auxiliary task. Unlike previous approaches, our method does not require ground truth data for the pixelwise relational probabilities or 3D models of the objects, which significantly expands the applicability in practical applications. Our results obtained using real-world data and human-robot experiments demonstrate the effectiveness of our method in reasoning about the best way to place objects to reproduce a spatial relation. Videos of our experiments can be found at https://youtu.be/zaZkHTWFMKM
