Zeng Gu

FCRF: Flexible Constructivism Reflection for Long-Horizon Robotic Task Planning with Large Language Models

Autonomous error correction is critical for domestic robots to achieve reliable execution of complex long-horizon tasks. Prior work has explored self-reflection in Large Language Models (LLMs) for task planning error correction; however, existing methods are constrained by inflexible self-reflection