Neuro-Symbolic Skill Discovery for Conditional Multi-Level Planning

Understand

This paper proposes a novel learning architecture for acquiring generalizable high-level symbolic skills from a few unlabeled low-level skill trajectory demonstrations.

• The architecture involves neural networks for symbol discovery and low-level controller acquisition and a multi-level planning pipeline that utilizes the discovered symbols and the learned low-level controllers.
• The discovered action symbols are automatically interpreted using visual language models that are also responsible for generating high-level plans.
• While extracting high-level symbols, our model preserves the low-level information so that low-level action planning can be carried out by using gradient-based planning.