外观
论文arXiv 2610.0739623 天前
高程图看不到什么：面向人形机器人的语义感知运动与执行感知导航
针对人形导航中高程图难辨隐患与执行偏差，提出基准和闭环VLN+运动控制框架，并在仿真与Unitree G1上验证。
宇树 G1原文链接：What the Elevation Map Cannot See: Semantic-Aware Locomotion and Execution-Aware Navigation for Humanoid Robot（arXiv 2610.07396 · 发布日期：2026-10-05）
What the Elevation Map Cannot See: Semantic-Aware Locomotion and Execution-Aware Navigation for Humanoid Robot
针对人形导航中高程图难辨隐患与执行偏差，提出基准和闭环VLN+运动控制框架，并在仿真与Unitree G1上验证。
核心要点
- 现有VLN基准虽可物理执行，但假设隐患可由高程图观测且运动贴近期望。
- 引入同时建模高程细微隐患与执行偏差的基准，用于暴露相关失败模式。
- 提出闭环VLN+运动控制框架，持续将高层导航与机器人实际状态重新对齐。
- 结果显示语义输入减少接触高程图难表示的隐患，抗偏差提升导航成功率。
验证范围与局限：导航评估在仿真中进行，真机仅在Unitree G1人形机器人上验证运动策略。
英文摘要 (Abstract)
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.
作者：Shunyu Yao, Songyang Liu, Dinghao Chen, Yuanyuan Lei, Shuai Li
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