畳み込みを超えて：エンドツーエンド距離学習による空間関係の汎化
Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning
物体間の空間関係を距離学習で捉え、未知の物体にも汎化できるようにする手法を提案。学習した距離空間上で勾配最適化により目標関係を再現する物体姿勢を求める。
著者: Philipp Jund, Andreas Eitel, Nichola Abdo, Wolfram Burgard
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
To operate intelligently in domestic environments, robots require the ability to understand arbitrary spatial relations between objects and to generalize them to objects of varying sizes and shapes. In this work, we present a novel end-to-end approach to generalize spatial relations based on distance metric learning. We train a neural network to transform 3D point clouds of objects to a metric space that captures the similarity of the depicted spatial relations, using only geometric models of the objects. Our approach employs gradient-based optimization to compute object poses in order to imitate an arbitrary target relation by reducing the distance to it under the learned metric. Our results based on simulated and real-world experiments show that the proposed method enables robots to generalize spatial relations to unknown objects over a continuous spectrum.
