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.

Learning and Using abstractions for Robot Planning

In this paper, we propose unified framework based on deep learning that learns sound abstractiosn for complex robot planning problems and uses it to efficiently perform hierarchical planning.

Anytime Task and Motion Policies for Stochastic Envrionments

In this paper, we provide and efficient abstraction based methods to compute task and motion policies for complex robotics task for stochastic environments.