Multiple Subgoals-guided Hierarchical Learning in Robot Navigation

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Abstract

Solving obstacle-clustered robotic navigation tasks via model-free reinforcement learning (RL) is challenging due to the extended decision horizon and sparse rewards. Previous work has demonstrated efficient learning with single subgoal-conditioned hierarchical approaches. The subgoal is the action from the high-level policy and it operates on the low-level module, which could invoke sub-optimal policy when the selected subgoal is suboptimal. This work introduces multiple subgoals-guided navigation (MSGN) which consists of a high-level multiple subgoals Planner and a low-level goal-conditioned RL Controller. By passing multiple subgoals to the low-level agent, MSGN could alleviate the suboptimal subgoal problem by transferring the subgoal selection process to the RL agent. At the same time, multiple subgoals could help the goal-conditioned RL agent better explore and understand the environment and task. We tested our method on the Safety Gym suite. The results verified that MSGN could achieve a higher success rate and lower collision cost compared to baselines.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9-14
Number of pages6
ISBN (Electronic)9781665481090
DOIs
Publication statusPublished - 18-Jan-2023
Event2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022 - Jinghong, China
Duration: 5-Dec-20229-Dec-2022

Publication series

Name2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

Conference

Conference2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
Country/TerritoryChina
CityJinghong
Period05/12/202209/12/2022

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