Article

FISRF: An Adaptive Reflection-Guided Framework for LLM-Based Robot Task Planning

FISRF proposes an agent-and-mentor architecture that estimates a robot's Zone of Proximal Development (ZPD) in task planning and adaptively selects reflection strategies and mentor guidance, mitigating the accumulation of erroneous experience during early interactions. Experiments in ALFWorld and on real-world robotic platforms show that the method reduces average error-correction rounds by 37% and redundant reasoning by 25.1%, while improving overall task success rate by 8%.

Part of the event: FISRF: Adaptively Guiding Robotic Task Planning Reflection with a Mentor Architecture

This page provides a summary and source link only.