基于被动观察的工业装配自动化中示范学习的最新技术
State-of-the-Art in Learning-by-Demonstration with Passive Observation for Industrial Assembly Automation
- RWTH Aachen University（亚琛工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文系统综述了工业装配中基于被动观察的示范学习技术，聚焦一次性方法，探讨感知架构与泛化，发现向对象中心感知的转变，以支持新产品的快速适应。
AI中文摘要：
示范学习（LbD）通过捕捉专家技能实现直观的机器人编程，这对于高混合、低产量制造中的敏捷性至关重要。本系统文献综述分析了用于工业装配过程的被动示范学习，重点关注感知架构以及所感知示范的泛化。我们特别研究了一次性方法，即仅需单个示范的情况。该综述评估了系统如何利用这些有限数据适应新的装配任务。我们识别出向以对象为中心的感知的转变，使得学习到的基元能够以最少的训练迁移到新的产品变体。
英文摘要：
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.
