用技能图谱让机器人自动组装并持续优化，省去大量人工编程。
Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations
- 用基于动词的技能图谱组织机器人能力，实现语义级规划与执行对接。
- 部署后可自动收集数据并闭环改进技能，支持迭代优化。
- 适合需要长期自适应的工业装配场景，提升系统可复用性。
传统机器人装配系统在集成新任务、适应新环境和持续提升性能方面需大量人工工程投入。本文提出一种基于技能图谱（Skill Graph）的自主集成与持续改进框架。技能图谱将机器人能力以动词形式组织，显式关联语义描述（动词与名词）与可执行策略、前置条件、后置条件及评估器。我们证明，技能图谱支持基于语义的技能规划，同时通过明确接口连接机器人控制器与感知模块，实现快速系统集成。部署后，同一图谱结构可支持系统化数据采集与闭环性能改进，实现技能及其组合的迭代优化。该方法统一了系统配置、执行、评估与学习流程，为构建可适应、可复用的机器人装配系统提供了可扩展路径。代码已开源：https://github.com/intelligent-control-lab/AIDF。
原文摘要 · Abstract (English)
Robotic assembly systems traditionally require substantial manual engineering effort to integrate new tasks, adapt to new environments, and improve performance over time. This paper presents a framework for autonomous integration and continuous improvement of robotic assembly systems based on Skill Graph representations. A Skill Graph organizes robot capabilities as verb-based skills, explicitly linking semantic descriptions (verbs and nouns) with executable policies, pre-conditions, post-conditions, and evaluators. We show how Skill Graphs enable rapid system integration by supporting semantic-level planning over skills, while simultaneously grounding execution through well-defined interfaces to robot controllers and perception modules. After initial deployment, the same Skill Graph structure supports systematic data collection and closed-loop performance improvement, enabling iterative refinement of skills and their composition. We demonstrate how this approach unifies system configuration, execution, evaluation, and learning within a single representation, providing a scalable pathway toward adaptive and reusable robotic assembly systems. The code is at https://github.com/intelligent-control-lab/AIDF.
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