APEX：ロボットマニピュレーションのための教師なし物体中心シーン分割と追跡
APEX: Unsupervised, Object-Centric Scene Segmentation and Tracking for Robot Manipulation
ロボットアームを含む多様な物体を扱う実世界シーンで、マスク正規化と高解像度エンコーダにより教師なしで物体の分割・追跡を行う潜在変数モデルAPEXを提案し、新データセットP2Dで従来手法を上回る性能を示した。
著者: Yizhe Wu, Oiwi Parker Jones, Martin Engelcke, Ingmar Posner
分類: cs.RO
原文アブストラクト
Recent advances in unsupervised learning for object detection, segmentation, and tracking hold significant promise for applications in robotics. A common approach is to frame these tasks as inference in probabilistic latent-variable models. In this paper, however, we show that the current state-of-the-art struggles with visually complex scenes such as typically encountered in robot manipulation tasks. We propose APEX, a new latent-variable model which is able to segment and track objects in more realistic scenes featuring objects that vary widely in size and texture, including the robot arm itself. This is achieved by a principled mask normalisation algorithm and a high-resolution scene encoder. To evaluate our approach, we present results on the real-world Sketchy dataset. This dataset, however, does not contain ground truth masks and object IDs for a quantitative evaluation. We thus introduce the Panda Pushing Dataset (P2D) which shows a Panda arm interacting with objects on a table in simulation and which includes ground-truth segmentation masks and object IDs for tracking. In both cases, APEX comprehensively outperforms the current state-of-the-art in unsupervised object segmentation and tracking. We demonstrate the efficacy of our segmentations for robot skill execution on an object arrangement task, where we also achieve the best or comparable performance among all the baselines.
