演化程序化技能网络

摘要

我们研究在开放型具身环境中持续获取技能的问题，这类环境要求智能体必须构建、优化并复用不断扩展的可执行技能库。我们提出程序化技能网络（PSN）框架，该框架将技能定义为可执行的符号化程序，形成可通过经验演化的组合式网络。PSN通过大语言模型实例化三大核心机制：（1）用于技能组合结构化故障定位的REFLECT机制；（2）采用成熟度感知更新门控的渐进式优化，在稳定可靠技能的同时保持对不确定性技能的可塑性；（3）基于回滚验证的规范化结构重构，维持网络紧凑性。我们进一步揭示PSN的学习动态与神经网络训练存在结构相似性。在MineDojo和Crafter平台上的实验表明，该方法在开放型任务分布中展现出鲁棒的技能复用能力、快速适应性和强泛化性能。\footnote{代码将开源发布}

English

We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable skills. We introduce the Programmatic Skill Network (PSN), a framework in which skills are executable symbolic programs forming a compositional network that evolves through experience. PSN defines three core mechanisms instantiated via large language models: (1)REFLECT for structured fault localization over skill compositions, (2) progressive optimization with maturity-aware update gating that stabilizes reliable skills while maintaining plasticity for uncertain ones, and (3) canonical structural refactoring under rollback validation that maintains network compactness. We further show that PSN's learning dynamics exhibit structural parallels to neural network training. Experiments on MineDojo and Crafter demonstrate robust skill reuse, rapid adaptation, and strong generalization across open-ended task distributions.\footnote{We plan to open-source the code.

演化程序化技能网络

Evolving Programmatic Skill Networks

摘要

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