权重还是技能？机器人学习技术综述：从动作预测权重到自主编写技能的机器人
原标题：Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills
AI 摘要
本综述将机器人学习领域划分为两条技术路线：将能力固化在冻结权重中的视觉-语言-动作（VLA）模型，以及以代码形式编写和优化自身技能的系统。文章提出了一个五级自改进框架，基于执行反馈、持久记忆和程序搜索的组合，将代码即策略方法从零样本合成到完全自改进循环进行分类。调查覆盖77个代表性系统，并指出商业机器人技能市场目前仅提供静态回放，面临适应、跨实体可移植性、安全验证和标准化等开放问题。
正文节选
Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills Abstract Robot learning is splitting into two bets: policies that bake competence into frozen weights (vision-language-action, or VLA, models), and agents that write and refine their own executable skills as code. This survey organises the field around that axis of weights versus skills. Its central analytical contribution is a deep-dive that arranges code-as-policy meth