外观
论文arXiv 2610.0958812 天前
用于控制机器人的自适应代码生成
原文链接：Adaptive Code Generation for Controlling Robots（arXiv 2610.09588 · 发布日期：2026-10-16）
Adaptive Code Generation for Controlling Robots
提出双AI机器人控制框架，用LLM生成受限可执行代码、VLM提供语义扎根，并在前沿模型基准中验证。
核心要点
- 未知动态环境要求机器人从固定指令库转向基于意图的自主控制。
- LLM将高层意图转为受形式化机器人库和可验证语法约束的程序代码。
- VLM通过蒸馏过程提供语义扎根，并结合几何与语义阈值触发重规划。
- 在前沿模型基准中，框架显示受约束的反应式循环可支持复杂意图执行。
英文摘要 (Abstract)
Abstract Deploying robots as Complex Adaptive Systems (CAS) in unknown and dynamic environments necessitates a transition from rigid command libraries toward intention-based autonomy, as natural language represents the only medium capable of articulating complex goals beyond the capacity of finite instruction sets. While Large Language Models (LLMs) offer a path toward natural language goal description, their integration introduces significant challenges: the formalization gap between imprecise intentions and executable actions, the taxonomy gap induced by unpredictable environments, and the challenge of maintaining temporal state and progress awareness. This work introduces an architectural framework that enables robotic control by leveraging generative AI. The system follows a dual-AI design: an LLM translates high-level intentions into executable program code restricted to a formal robotic library and constrained by verifiable syntax, while a Vision-Language Model (VLM) provides semantic grounding via a distillation process. To ensure robustness, the framework incorporates environment-driven replanning triggers based on geometric and semantic thresholds, complemented by continuous runtime monitoring and an adaptive planning loop. Benchmarked across frontier models, our framework architecture demonstrates that grounding generative AI in a reactive, constrained loop enables robust fulfillment of complex intentions in dynamic and unknown environments.
作者：Justus Flerlage, Thorsten Wittkopp, Alexander Acker, Odej Kao
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