FLTRNN: Faithful Long-Horizon Task Planning for Robotics with Large Language Models
Jiatao Zhang, Lanling Tang, Yufan Song, Qiwei Meng, Haofu Qian, Jun Shao, Wei Song, Shiqiang Zhu
Abstract
Recent planning methods based on Large Language Models typically employ the In-Context Learning paradigm. Complex long-horizon planning tasks require more context(including instructions and demonstrations) to guarantee that the generated plan can be executed correctly. However, in such conditions, LLMs may overlook(unfaithful) the rules in the given context, resulting in the generated plans being invalid or even leading to dangerous actions. In this paper, we investigate the faithfulness of LLMs for complex long-horizon tasks. Inspired by human intelligence, we introduce a novel framework named FLTRNN. FLTRNN employs a language-based RNN structure to integrate task decomposition and memory management into LLM planning inference, which could effectively improve the faithfulness of LLMs and make the planner more reliable. We conducted experiments in VirtualHome household tasks. Results show that our model significantly improves faithfulness and success rates for complex long-horizon tasks. Website at https://tannl.github.io/FLTRNN.github.io/
BibTeX
@inproceedings{icra2024_fltrnnfaithfullo,
title = {FLTRNN: Faithful Long-Horizon Task Planning for Robotics with Large Language Models},
author = {Jiatao Zhang and Lanling Tang and Yufan Song and Qiwei Meng and Haofu Qian and Jun Shao and Wei Song and Shiqiang Zhu and Jason Gu},
booktitle = {ICRA 2024},
year = {2024}
}