neuro-symbolic AI

Hierarchical Planning and Learning for Robots in Stochastic Settings Using Zero-Shot Option Invention

This paper proposes a new method for robots to plan actions in complex environments, even when the environment is unknown. The robot learns to create its own high-level actions without needing pre-programmed ones. This allows the robot to quickly solve new problems in unseen environments. The method is shown to be faster and achieve significantly better solutions than existing approaches

Using Deep Learning to Bootstrap Abstractions for Robot Planning

In this paper, we use deep learning to identify critical regions and automatically construct hierarchical state and action abstractions. We use these hierarchical abstractions with a multi-source mutli-directional hierarchical planner to compute solutions for robot planning problem.