Skill-SLM: Agent Skill-driven Small Language Models for Reliable Robot Operation
Overview
Skill-SLM redefines robot operation using task decomposition and skill composition, outperforming distillation-based methods on unseen tasks. Experiments on UAVs show improved generalization capabilities using LLM teachers for data synthesis and grammar-based skill extraction.
Generated from attributed reports · Updated 1 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSkill-SLM: Agent Skill-driven Small Language Models for Reliable Robot Operation
This paper presents Skill-SLM, a framework that reformulates small language model-driven robot operation as a task-decomposition and skill-composition problem. It introduces a novel context-free grammar to extract skills and uses LLM teachers to synthesize training data, enabling reliable task decomposition and skill orchestration. Experiments on UAV and ground vehicle tasks show significant improvements over distillation-based baselines, especially on unseen tasks requiring generalization.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).