Adaptive Code Generation for Controlling Robots
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 frame...
| Authors | Justus Flerlage, Thorsten Wittkopp, Alexander Acker et al. |
|---|---|
| Primary category | cs.RO |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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