From Words to Waypoints: Teaching a Small LLM to Plan for Robots (www.daweisun.me)

Researchers have explored the use of a small language model, Qwen3.5-9B, to plan robotic movements by interpreting tasks described in Signal Temporal Logic (STL). Traditionally, robot planning involves encoding tasks into mixed-integer linear programs (MILP) and relying on solvers, which can be inefficient as task complexity increases. The new approach leverages the language model's ability to understand the semantics of STL, facilitating direct path planning without the lengthy computational overhead of MILP solvers. The study highlights the model's development process using reinforcement learning (RL), faced with early failures where it struggled to learn effectively from the tasks. Researchers eventually succeeded by providing the model with computational tools to assist with calculations, allowing it to focus on high-level strategic planning rather than arithmetic details. This combination of language understanding and computational assistance demonstrates a promising shift towards utilizing smaller models in robotic planning tasks, potentially lowering the dependency on larger models while enriching the capabilities of AI systems in understanding and executing complex task specifications.