Work

Hierarchical Reinforcement Learning for Navigation Among Movable and Immovable Obstacles

Year: 2025

Type: article

Abstract: Path planning is a key technique for vehicle navigation, and significant research has focused on how to manage obstacles. Previous methods have addressed either removing movable obstacles or avoiding immovable ones. However, real-world environments contain both movable and immovable obstacles. To address this, we propose a path planning system for Navigation Among Movable and IMmovable Obstacles (NAMIMO) based on hierarchical reinforcement learning. In our system, lower-level agents generate paths for the vehicle to either avoid or remove obstacles, while the higher-level agent dynamically selects which lower-level agent to utilize based on the movability of the obstacle, incorporating visual and linguistic knowledge.

Source: IEEE Access

DOI: 10.1109/access.2025.3598230

Authors: Han Jun Bae, Juyoun Park

Institutions: Samsung Electronics (South Korea), Korea Institute of Science and Technology

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: Korea Institute of Science and Technology, Korea Institute of Science and Technology Information