受動的観察によるデモンストレーション学習の産業組立自動化における最新動向
State-of-the-Art in Learning-by-Demonstration with Passive Observation for Industrial Assembly Automation
産業組立における受動的観察を用いたデモンストレーション学習（LbD）の系統的レビューであり、特に単一デモンストレーションでの一般化と物体中心知覚への移行を分析している。
著者: David Koetter, Oliver Petrovic, Christian Brecher
分類: cs.RO
原文アブストラクト
Learning-by-Demonstration (LbD) enables intuitive robot programming by capturing expert skills, which is crucial for agility in high-mix, low- volume manufacturing. This systematic literature review analyzes passive LbD for industrial assembly processes, focusing on the perception architecture and the generalization of the perceived demonstration. We specifically investigate one-shot approaches where only a single demonstration is required. The review evaluates how systems adapt to new assemblies using this limited data. We identify a shift towards object-centric perception, allowing learned primitives to be transferred to new product variants with minimal training.