SEAL: SEmantic-Augmented Imitation Learning via Language Model

• URL: http://arxiv.org/abs/2410.02231v1
• Date: Thu, 3 Oct 2024 05:53:10 GMT
• Title: SEAL: SEmantic-Augmented Imitation Learning via Language Model
• Authors: Chengyang Gu, Yuxin Pan, Haotian Bai, Hui Xiong, Yize Chen,
• Abstract summary: SEAL is a novel framework for specifying sub-goal space and pre-labeling states to semantically meaningful sub-goal representations. SEAL employs a dual-encoder structure, combining supervised LLM-guided sub-goal learning with unsupervised Vector Quantization. Our experiments demonstrate that SEAL outperforms state-of-the-art HIL methods and LLM-based planning approaches.
• Score: 16.345410506033915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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