仅用一次演示教会机器人完成工业装配，还能快速适配新产品。
State-of-the-Art in Learning-by-Demonstration with Passive Observation for Industrial Assembly Automation
- 基于单次示范学习，通过物体为中心的感知架构捕捉操作技能
- 新装配任务仅需少量数据即可实现有效泛化，适应性显著提升
- 适合高混合低批量生产场景，降低机器人编程门槛
学习式示范（LbD）通过捕获专家技能实现直观的机器人编程，对高混合、低批量制造中的敏捷性至关重要。本文系统综述了工业装配中被动式学习式示范的研究进展，重点分析感知架构与示范泛化能力。特别关注仅需单次示范的一次性方法，评估系统如何利用有限数据应对新装配任务。研究发现，感知范式正从任务导向转向以物体为中心，使学习到的操作基元能迁移至新型产品变体，且所需训练极少。
原文摘要 · Abstract (English)
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.
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