Conventional navigation pipelines for legged robots remain largely geometry-centric, relying on dense SLAM representations that are fragile under rapid motion and offer limited support for semantic decision making in open-world exploration. In this work, we focus on decision-driven semantic object exploration, where the primary challenge is not map consistency but how noisy and heterogeneous semantic observations can be transformed into stable and executable exploration decisions. We propose a vision-based approach that explicitly addresses this problem through confidence-calibrated semantic evidence arbitration, a controlled-growth semantic topological memory, and a semantic utility-driven subgoal selection mechanism. These components enable the robot to accumulate task-relevant semantic knowledge over time and select exploration targets that balance semantic relevance, reliability, and reachability, without requiring dense geometric reconstruction. Extensive experiments in both simulation and real-world environments demonstrate that the proposed mechanisms consistently improve the quality of semantic decision inputs, subgoal selection accuracy, and overall exploration performance on legged robots.

翻译：传统的腿式机器人导航流程仍以几何信息为核心，依赖稠密SLAM表征。这类方法在快速运动下鲁棒性较差，且对开放世界探索中的语义决策支持有限。本研究聚焦于决策驱动的语义目标探索，其核心挑战并非地图一致性，而是如何将含噪声的异构语义观测转化为稳定可执行的探索决策。我们提出一种基于视觉的方法，通过置信度校准的语义证据仲裁机制、受控增长的语义拓扑记忆以及语义效用驱动的子目标选择机制，系统性地解决该问题。这些组件使机器人能够持续积累任务相关的语义知识，并在无需稠密几何重建的前提下，选择平衡语义相关性、可靠性与可达性的探索目标。在仿真与真实环境中的大量实验表明，所提机制能持续提升语义决策输入质量、子目标选择精度以及腿式机器人的整体探索性能。