发表机构
Southern University of Science and Technology; Guangdong Direct Drive Technology Limited; South China University of Technology; Great Bay University（南方科技大学; 广东直驱技术有限公司; 华南理工大学; 大湾区大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对目标导航任务中现有方法局限，提出ZONDA框架，集成启发式多楼层规划、多视图目标验证、动态行人避障三个核心组件，通过真实机器人及模拟测试取得显著改进结果，在动态基准测试中表现优于现有基线。
AI 中文摘要
在目标导航任务中，现有方法通常局限于静态单楼层环境，忽视跨楼层拓扑和动态行人，限制了实际应用。为此提出ZONDA零样本目标导航与动态避障框架，集成启发式多楼层规划（从高度差可遍历地图实现楼梯穿越和跨楼层探索）、多视图目标验证（用视觉语言模型交叉检查多尺度观测减少误报）、动态行人避障（明确跟踪预测行人以生成预期行为）三个核心组件。在真实机器人及模拟环境测试中，ZONDA取得显著改进结果，在动态基准测试中也比现有基线表现更优。
英文摘要
In Object Goal Navigation task, existing methods are typically restricted to static and single-floor environments, ignoring cross-floor topologies and dynamic pedestrian, which limits their real-world deployment. To address these limitations, we propose ZONDA, a zero-shot object navigation with dynamic avoidance framework. In particular, ZONDA integrates three core components: (i) Heuristic multi-floor planning: from height-difference traversable maps, enables stair traversal and cross-floor exploration without a platform-specific learned controller; (ii) Multi-view target verification: cross-checks multi-scale observations with a vision-language model, significantly reducing false positives; and (iii) Dynamic pedestrian avoidance: explicitly tracks and predicts moving pedestrians to generate anticipatory behaviors. Evaluated on a real Direct Drive Tech TITA biped robot and extensive simulations on HM3D and MP3D, ZONDA achieves significantly improved results. Moreover, ZONDA can maintain robust navigation on the dynamic benchmark HM3D-DYNA compared to the existing baseline.
