发表机构
University of Florida（佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有VLN基准忽略高程图无法辨识的危险及执行偏差的问题，提出含高程细微危险与执行偏差的基准及闭环VLN+运动控制框架，在仿真和Unitree G1上验证，证明语义输入与抗偏差提升导航表现。
AI 中文摘要
人形机器人的导航至关重要，然而，由于成本和安全性问题，在物理硬件上进行大规模评估往往不切实际，这使得仿真基准测试变得必不可少。现有的视觉语言导航（VLN）基准测试实现了物理上可执行的导航，但仍假设：（1）所有危险都能从高程图中观察到；（2）实际运动与期望运动高度匹配。然而，在真实环境中，倒下的瓶子在高程图中可能难以辨识，而手机和水渍也可能难以区分；运动策略的危险规避可能导致机器人的实际轨迹偏离VLN策略所规划的路径。这种命令执行不匹配可能会累积，并导致机器人走向非预期位置。为了暴露这些故障模式，我们引入了一个同时模拟高程细微危险和执行偏差的基准测试，并构建了一个闭环的VLN与运动控制框架，该框架持续将高层导航与机器人的实际状态重新对齐。我们在仿真中评估了导航性能，并进一步在物理Unitree G1人形机器人上验证了运动策略。结果表明，语义输入减少了与高程图中表示不佳的危险的接触，而抗偏差机制提高了导航成功率。这些发现强调了需要从路径完成和危险规避两方面联合评估人形机器人导航的必要性。
英文摘要
Navigation for humanoid robots is critical, yet large-scale evaluation on physical hardware is often impractical due to cost and safety concerns, making simulation benchmarks essential. Existing VLN benchmarks achieve physically executable navigation, but still assume (1) all hazards are observable from elevation maps; (2) realized motions closely match desired motions. In real environments, however, fallen bottles may be ambiguous in elevation maps, while phones and water spills may be difficult to differentiate; hazard avoidance by the locomotion policy can cause the robot's actual trajectory to deviate from the path intended by the VLN policy. Such command-execution mismatch can accumulate and lead the robot toward unintended locations. To expose these failure modes, we introduce a benchmark that models both elevation-subtle hazards and execution deviations, together with a closed-loop VLN + locomotion control framework that continuously realigns high-level navigation with the robot's actual state. We evaluate navigation in simulation and further validate the locomotion policy on a physical Unitree G1 humanoid robot. Results show that semantic input reduces contact with hazards poorly represented in elevation maps, while anti-deviation improves navigation success. These findings highlight the need to evaluate humanoid navigation jointly in terms of route completion and hazard avoidance.
