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

Created by
  • Haebom

Author

Yufan Song, Jiatao Zhang, Zeng Gu, Qingmiao Liang, Tuocheng Hu, Wei Song, Shiqiang Zhu

Outline

This paper emphasizes the importance of autonomous error correction for reliable performance of complex and long-term tasks by domestic robots. Previous studies on task planning error correction using large-scale language models (LLMs) have been limited in their efficiency due to their inflexible self-reflection mechanisms. Inspired by human cognitive adaptation, in this paper, we propose a novel mentor-agent architecture, the Flexible Constructivism Reflection Framework (FCRF), that enables flexible self-reflection according to task difficulty. FCRF constructively integrates past success and failure experiences. Through evaluations on various domestic tasks in AlfWorld simulation and real environments, we experimentally demonstrate that FCRF significantly improves the overall performance and flexibility of self-reflection in complex and long-term robotic tasks.

Takeaways, Limitations

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Takeaways:
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A novel approach to improve the performance of autonomous robot error correction based on LLM
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Proof of the utility of FCRF architecture that performs flexible self-reflection according to task difficulty
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Reliability is ensured through verification through simulation and experiments in real environments.
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Improving robots' learning and adaptation capabilities by mimicking human cognitive adaptation processes
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Limitations:
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Only AlfWorld simulations and evaluation results in specific real-world environments are presented, requiring further research on generalizability.
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Further research is needed on the scalability of FCRF and its applicability to various task types.
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Further research is needed on the ability to cope with unexpected situations that may occur in real-world settings.
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