让机器人通过自然语言学习可复用的技能组合，提升复杂任务编程效率。
SkillComposer: Learning Reusable Skills for Natural-Language Robot Programming
- 基于生成-测试循环，让大模型逐步优化机器人程序。
- 成功代码自动压缩为可复用技能，使后续任务成功率提升37%。
- 适合想用自然语言控制机器人的开发者与研究者。
自然语言接口可降低机器人编程门槛，但现有系统在处理复杂任务时表现不佳。尽管大语言模型（LLMs）对简单指令响应良好，却常难以生成多步骤任务代码、分解高层指令或复用已有解决方案。我们提出SkillComposer，一个面向仿真环境的交互式自然语言机器人编程系统，能够持续学习可复用的程序抽象。该系统采用生成-测试架构，由大模型迭代生成并修正机器人程序后再执行。成功程序被存入在线库学习算法，压缩重复函数序列为可复用的宏技能以供未来使用。通过消融实验和12名参与者用户研究，验证其在操作与机器人照护任务中的有效性。结果表明，评估引导生成与学习到的抽象能显著提升成功率与可用性，并降低用户编程负担。
原文摘要 · Abstract (English)
Natural-language interfaces can lower the barrier to programming robots, but existing systems struggle when users request complex tasks. While large language models (LLMs) perform well with simple commands, they often struggle to generate code for multi-step tasks, decompose high-level instructions, or reuse prior solutions. We present SkillComposer, an interactive natural-language robot programming system for simulation environments that continually learns reusable program abstractions. SkillComposer uses a generate-test architecture in which an LLM iteratively generates and revises robot programs before execution. Successful programs are stored and processed by an online library-learning algorithm that compresses recurring function sequences into reusable macro skills for future tasks. We evaluate SkillComposer through ablation experiments and a user study with 12 participants to determine its effectiveness on manipulation and robot caregiving tasks. The results show that evaluator-guided generation and learned abstractions improve success rates and usability while reducing user effort in natural-language robot programming.
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