Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning

Kyowoon Lee*, Seongun Kim*, Jaesik Choi

ICRA 2023

(*: equal contribution)

[Paper] [Code] [Simulator] [Poster]

Abstract

For robotic vehicles to navigate robustly and safely in unseen environments, it is crucial to decide the most suitable navigation policy. However, most existing deep reinforcement learning based navigation policies are trained with a hand-engineered curriculum and reward function which are difficult to be deployed in a wide range of real-world scenarios. In this paper, we propose a framework to learn a family of low-level navigation policies and a high-level policy for deploying them. The main idea is that, instead of learning a single navigation policy with a fixed reward function, we simultaneously learn a family of policies that exhibit different behaviors with a wide range of reward functions. We then train the high-level policy which adaptively deploys the most suitable navigation skill. We evaluate our approach in simulation and the real world and demonstrate that our method can learn diverse navigation skills and adaptively deploy them. We also illustrate that our proposed hierarchical learning framework presents explainability by providing semantics for the behavior of an autonomous agent.

Contribution

• Proposal of a hierarchical reinforcement learning approach which learns diverse navigation skills and deploys them.
• Extensive evaluation of our approach on various scenarios including a real-world robot navigation which demonstrates effectiveness and explainability of our approach.

Reference

@article{lee2023adaptive,
  title={Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning},
  author={Lee, Kyowoon and Kim, Seongun and Choi, Jaesik},
  journal={arXiv preprint arXiv:2305.19746},
  year={2023}
}