Researchers Develop Framework for Efficient Skill Grounding in Robots Using Small Language Models
Researchers have introduced RECENT, a framework that enables small language models (sLMs) to effectively ground robotic skills across different embodiments and environments by refactoring executable code rather than regenerating it from scratch. The approach addresses a key challenge in embodied AI: adapting reusable skills when robots or environments differ, which typically requires large language models that are impractical for on-device deployment. The work demonstrates that sLM-based systems can match the performance of LLM-based approaches while remaining computationally efficient for real-world robotic applications.
A new research paper on arXiv presents RECENT, a refactoring-centric agent framework designed to improve how robotic systems adapt skills across different physical embodiments and environments. The core innovation involves representing skills as executable code and modifying only the execution bindings through localized refactoring, rather than regenerating entire code sequences. This approach decouples the semantic intent of a skill from embodiment- and environment-specific details, allowing small language models to handle skill grounding effectively. The researchers evaluated RECENT across multiple robot embodiments in dynamic environments, demonstrating robust long-horizon task performance. The framework achieves the best performance among small language model-based Code-as-Policies methods and matches the task performance of approaches using larger language models, suggesting a practical path toward efficient on-device robotic control without relying on computationally expensive large models.
What's missing
The paper does not specify which robot embodiments were tested, the specific dynamic environments used in evaluation, or provide quantitative performance metrics comparing RECENT to baseline methods. Additionally, limitations of the refactoring approach and failure cases are not discussed in the abstract.
What different sources said
- arXiv cs.AICenter
Language-based Trial and Error Falls Behind in the Era of Experience
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