Skill-SLM: Agent Skill-driven Small Language Models for Reliable Robot Operation

Key points

• Small language models (SLMs) have been increasingly adopted for onboard robot operation because they enable intelligent decision-making.1
• This paper proposes Skill-SLM, a framework that reformulates SLM-driven robot operation as a task-decomposition and skill-composition problem.1
• Given a natural language task instruction, Skill-SLM decomposes the task into subtasks, selects appropriate skills from the skill library, and orchestrates the selected skills into executable robot operations.1
• Experiments on UAV operation tasks indicate that Skill-SLM substantially outperforms distillation-oriented baselines, especially on unseen tasks that require generalization of capabilities.1

Sources (1)

• [1]Skill-SLM: Agent Skill-driven Small Language Models for Reliable Robot Operation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 07:17 PM

  First, to support the skill-driven workflow, we propose a novel robot operational skill aware context-free grammar (CFG) to extract the skills required to accomplish tasks and build the skill library accordingly.

  Small language models (SLMs) have been increasingly adopted for onboard robot operation because they enable intelligent decision-making.

Extractive summary: sentences quoted from the sources.