SEAL: SEmantic-Augmented Imitation Learning via Language Model
<|reference_start|>SEAL: SEmantic-Augmented Imitation Learning via Language Model: Hierarchical Imitation Learning (HIL) is a promising approach for tackling long-horizon decision-making tasks. While it is a challenging task due to the lack of detailed supervisory labels for sub-goal learning, and reliance on hundreds to thousands of expert demonstrations. In this work, we introduce SEAL, a novel framework that leverages Large Language Models (LLMs)'s powerful semantic and world knowledge for both specifying sub-goal space and pre-labeling states to semantically meaningful sub-goal representations without prior knowledge of task hierarchies. SEAL employs a dual-encoder structure, combining supervised LLM-guided sub-goal learning with unsupervised Vector Quantization (VQ) for more robust sub-goal representations. Additionally, SEAL incorporates a transition-augmented low-level planner for improved adaptation to sub-goal transitions. Our experiments demonstrate that SEAL outperforms state-of-the-art HIL methods and LLM-based planning approaches, particularly in settings with small expert datasets and complex long-horizon tasks.<|reference_end|>
@article{gu2024seal:,
    title={SEAL: SEmantic-Augmented Imitation Learning via Language Model},
    author={Chengyang Gu, Yuxin Pan, Haotian Bai, Hui Xiong, Yize Chen},
    journal={arXiv preprint arXiv:2410.02231},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02231},
    primaryClass={cs.AI cs.LG cs.SY eess.SY}
}