机器人正在分裂成两派：背熟动作，还是自己写技能
机器人学习正在分裂成两个赌注：一派把能力烤进冻结的权重里（视觉-语言-动作模型），另一派让机器人自己写代码、改代码、攒技能。这篇综述把77个系统按“自我改进程度”排成一条线：从零样本生成程序，到闭环自我修复，再到能记住技能并进化搜索——最后那个格子几乎没人占，只有ASPIRE、ENPIRE、RoboClaw几个新系统挤进去。它还戳破一个词：大家嘴上都说“技能”，其实至少五种意思，只有“写代码”那种能不用梯度更新就自我改进。商业技能市场已经在卖“一键技能”，但只发静态回放，适应、跨机器人迁移、安全验证全是坑。这不是你明天能用上的东西，但如果你在赌机器人下一步往哪走，这是地图。
📄 原文摘要(英文)
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 methods by their degree of self-improvement, from zero-shot program synthesis, through closed-loop self-repair and persistent skill memory, to the sparsely populated cell in which execution feedback, skill memory, and evolutionary search combine into one open-ended loop; only a few very recent systems (for example ASPIRE, ENPIRE, and RoboClaw) occupy that cell. We map the complementary "skills" pole, from unsupervised reinforcement-learning skill discovery to large-language-model skill libraries, and show that the word "skill" is used in at least five distinct senses, of which only the code sense self-improves without gradient updates. We then connect the taxonomy to the emerging skill economy: commercial robot-skill marketplaces now distribute one-tap skills across robots but ship only static playback, which surfaces open problems of adaptation, cross-embodiment portability, provenance, safety verification, composition, and standardisation. This is a deliberately focused survey. Rather than cataloguing the field exhaustively, it examines 77 representative systems across six technique families through one taxonomy and a set of contrast tables, and it supplies operational definitions of the self-improvement mechanisms together with a statement of what each family cannot do.