HANDLOOM: 検査と操作のための一次元物体の学習ベース追跡
HANDLOOM: Learned Tracing of One-Dimensional Objects for Inspection and Manipulation
多数の交差を持つケーブルなどの変形可能な線状物体の軌跡を、ニューラルネットワークでグレースケール画像から推定し、結び目認識やロボットによる結び・ほどき操作を可能にする手法を提案。
著者: Vainavi Viswanath, Kaushik Shivakumar, Jainil Ajmera, Mallika Parulekar, Justin Kerr, Jeffrey Ichnowski, Richard Cheng, Thomas Kollar, Ken Goldberg
分類: cs.RO
原文アブストラクト
Tracing - estimating the spatial state of - long deformable linear objects such as cables, threads, hoses, or ropes, is useful for a broad range of tasks in homes, retail, factories, construction, transportation, and healthcare. For long deformable linear objects (DLOs or simply cables) with many (over 25) crossings, we present HANDLOOM (Heterogeneous Autoregressive Learned Deformable Linear Object Observation and Manipulation), a learning-based algorithm that fits a trace to a greyscale image of cables. We evaluate HANDLOOM on semi-planar DLO configurations where each crossing involves at most 2 segments. HANDLOOM makes use of neural networks trained with 30,000 simulated examples and 568 real examples to autoregressively estimate traces of cables and classify crossings. Experiments find that in settings with multiple identical cables, HANDLOOM can trace each cable with 80% accuracy. In single-cable images, HANDLOOM can trace and identify knots with 77% accuracy. When HANDLOOM is incorporated into a bimanual robot system, it enables state-based imitation of knot tying with 80% accuracy, and it successfully untangles 64% of cable configurations across 3 levels of difficulty. Additionally, HANDLOOM demonstrates generalization to knot types and materials (rubber, cloth rope) not present in the training dataset with 85% accuracy. Supplementary material, including all code and an annotated dataset of RGB-D images of cables along with ground-truth traces, is at https://sites.google.com/view/cable-tracing.
