机器人的上下文学习：方法与应
In-Context Learning for Robots: Methods and Applications
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中文总结 AI 辅助
本文综述了机器人上下文学习（ICL）的方法与应用，按接口分类四种方法，分析迁移假设与评估实践，提出组合任务获取与物理递归自我改进的研究议程。
中文摘要 AI 辅助
通用机器人必须推断新任务的要求，并将这种理解转化为适当的物理动作。机器人的上下文学习（ICL）通过使用示范和交互来支持这一过程，在部署期间保持神经参数固定，从而引导现有的能力。我们围绕连接上下文证据与执行的接口组织这篇文献综述，区分了四个家族：上下文条件策略、几何示范迁移、基于世界模型的控制以及基于技能和智能体的执行。比较这些接口阐明了它们的迁移假设，以及训练、对应和记忆在使上下文有用中的作用。在操作和导航中，我们考察了这些机制如何在对象、环境和执行条件变化时保持所教授的要求。这一分析将方法设计与评估实践联系起来，这些实践区分了对教学的反应性、物理迁移以及从保留经验中获得的益处。由此产生的议程将组合任务获取和忠实迁移与物理递归自我改进联系起来，其中经验提高了学习后续任务的能力。
英文摘要
General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action. In-context learning (ICL) for robots supports this process by using demonstrations and interaction to direct existing competence with neural parameters held fixed during deployment. We organize this literature review around the interfaces connecting contextual evidence to execution, distinguishing four families: context-conditioned policies, geometric demonstration transfer, world-model-based control, and skill- and agent-based execution. Comparing these interfaces clarifies their transfer assumptions and the roles of training, correspondence, and memory in making context useful. Across manipulation and navigation, we examine how these mechanisms preserve taught requirements as objects, environments, and execution conditions change. This analysis links method design to evaluation practices that distinguish responsiveness to teaching, physical transfer, and benefits from retained experience. The resulting agenda connects compositional task acquisition and faithful transfer with physical recursive self-improvement, in which experience improves the ability to learn subsequent tasks.
