日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

マニピュレーションarXiv:2008.05626

布の領域分割によるロバストな把持選択

Cloth Region Segmentation for Robust Grasp Selection

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深度画像から布の端や角をしわ・折り目と区別してセグメンテーションし、把持位置・方向・不確かさを推定する手法を提案し、実機で把持成功率が向上することを示した。

著者: Jianing Qian, Thomas Weng, Luxin Zhang, Brian Okorn, David Held

分類: cs.RO

原文アブストラクト

Cloth detection and manipulation is a common task in domestic and industrial settings, yet such tasks remain a challenge for robots due to cloth deformability. Furthermore, in many cloth-related tasks like laundry folding and bed making, it is crucial to manipulate specific regions like edges and corners, as opposed to folds. In this work, we focus on the problem of segmenting and grasping these key regions. Our approach trains a network to segment the edges and corners of a cloth from a depth image, distinguishing such regions from wrinkles or folds. We also provide a novel algorithm for estimating the grasp location, direction, and directional uncertainty from the segmentation. We demonstrate our method on a real robot system and show that it outperforms baseline methods on grasping success. Video and other supplementary materials are available at: https://sites.google.com/view/cloth-segmentation.

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