VQ-CNMP: Neuro-Symbolic Skill Learning for Bi-Level Planning

Published: 24 Oct 2024, Last Modified: 06 Nov 2024LEAP 2024 PosterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Skill Discovery, Neuro-Symbolic Robotics, Multi-Level Planning, Task and Motion Planning
TL;DR: This paper proposes a novel neural network model capable of discovering high-level skill representations from unlabeled demonstration data. We also propose a bi-level planning pipeline that utilizes our model using a gradient-based planning approach.
Abstract: This paper proposes a novel neural network model capable of discovering high-level skill representations from unlabeled demonstration data. We also propose a bi-level planning pipeline that utilizes our model using a gradient-based planning approach. While extracting high-level representations, our model also preserves the low-level information, which can be used for low-level action planning. In the experiments, we tested the skill discovery performance of our model under different conditions, tested whether Multi-Modal LLMs can be utilized to label the learned high-level skill representations, and finally tested the high-level and low-level planning performance of our pipeline.
Submission Number: 45
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