TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments
Jumman Hossain, Abu-Zaher Faridee, Nirmalya Roy, Jade Freeman, Timothy, Gregory, Theron T. Trout

TL;DR
TopoNav introduces a topological navigation framework that combines active mapping, hierarchical reinforcement learning, and intrinsic motivation to improve exploration efficiency in sparse reward environments for autonomous robots.
Contribution
It presents a novel topological navigation approach integrating hierarchical policies and intrinsic motivation, enabling effective exploration without prior maps or dense rewards.
Findings
Increased exploration coverage by 7-20%.
Success rates improved by 9-19%.
Navigation times reduced by 15-36%.
Abstract
Autonomous robots exploring unknown environments face a significant challenge: navigating effectively without prior maps and with limited external feedback. This challenge intensifies in sparse reward environments, where traditional exploration techniques often fail. In this paper, we present TopoNav, a novel topological navigation framework that integrates active mapping, hierarchical reinforcement learning, and intrinsic motivation to enable efficient goal-oriented exploration and navigation in sparse-reward settings. TopoNav dynamically constructs a topological map of the environment, capturing key locations and pathways. A two-level hierarchical policy architecture, comprising a high-level graph traversal policy and low-level motion control policies, enables effective navigation and obstacle avoidance while maintaining focus on the overall goal. Additionally, TopoNav incorporates…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Robotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques
MethodsFocus
