TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments

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 intrinsic motivation to guide exploration toward relevant regions and frontier nodes in the topological map, addressing the challenges of sparse extrinsic rewards. We evaluate TopoNav both in the simulated and real-world off-road environments using a Clearpath Jackal robot, across three challenging navigation scenarios: goal-reaching, feature-based navigation, and navigation in complex terrains. We observe an increase in exploration coverage by 7- 20%, in success rates by 9-19%, and reductions in navigation times by 15-36% across various scenarios, compared to state-of-the-art methods