  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
