发表机构
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SkillComposer是一款仿真环境下的交互式自然语言机器人编程系统，采用生成-测试架构，通过在线库学习算法生成可复用宏技能，经实验验证可提升任务成功率与可用性并减少用户工作量。
AI 中文摘要
自然语言界面可降低机器人编程的门槛，但现有系统在用户请求复杂任务时表现不佳。尽管大语言模型（LLMs）在简单指令上表现良好，但它们往往难以生成多步骤任务的代码、分解高级指令或复用先前的解决方案。我们提出SkillComposer，这是一款面向仿真环境的交互式自然语言机器人编程系统，可持续学习可复用的程序抽象。SkillComposer采用生成-测试架构，其中LLM在执行前迭代生成并修正机器人程序。成功的程序会被存储，并由在线库学习算法处理，该算法将重复出现的函数序列压缩为可复用的宏技能，供未来任务使用。我们通过消融实验和包含12名参与者的用户研究评估SkillComposer，以确定其在操纵和机器人护理任务上的有效性。结果表明，评估者引导的生成和学习到的抽象可提高成功率和可用性，同时减少自然语言机器人编程中的用户工作量。
英文摘要
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.
Comments8 pages, 6 figures. Submitted to IEEE Humanoids 2026
