建設現場におけるゼロショット学習による接触リッチなロボット操作：拡散ポリシー誘導型適応制御
Contact-Rich Robotic Manipulation in Construction via Zero-Shot Learning: A Diffusion Policy-Guided Adaptive Control
シミュレーションで学習した拡散ポリシーと適応制御を組み合わせ、建設現場の接触を伴う組立作業をゼロショットで実現するフレームワークを提案した。
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著者: Roman Ibrahimov, Salma Mozaffari, Arash Adel
分類: cs.RO
原文アブストラクト
Construction robotics and automation offer promising means of improving productivity, alleviating workforce shortages, and reducing workers' exposure to physically demanding tasks. However, reliable contact-rich robotic assembly remains challenging under tight tolerances, fabrication inaccuracies, and uncertain contact dynamics. To address this challenge, we present a framework coupling diffusion policies trained on simulation-generated pose and force/torque data with an L1-inspired adaptive controller that corrects policy-predicted actions online to compensate for unmodeled contact dynamics. We benchmark the framework against baselines in timber joinery, pipe fitting, and sequential full-scale truss assembly. It achieves 100% success on single-task assemblies and 90-100% success across sequential truss assembly subtasks, with lower, more stable contact forces than the baselines. By enabling zero-shot sim-to-real transfer for force-aware contact-rich assembly, the framework reduces costly, labor-intensive real-world data collection for policy training and advances scalable, robust automation of multistage assembly, motivating extension to broader contact-rich manipulation tasks in construction.