这篇论文解决了机器人导航中固定安全边距不准的问题，用上下文条件评判器动态调整，在HM3D和MP3D上跑出最高成功率，还能直接部署到人形机器人上。
论文提出上下文条件安全评判器，用于排名扩散规划器生成的轨迹候选。该方法分解为三个互补项：安全项包含清除预算惩罚和控制障碍函数残差，效率项结合平滑度惩罚和安全门控绕路率惩罚，距离约束匹配项防止边距坍缩。在HM3D和MP3D的PointGoal导航中，该方法在成功率和成功加权路径长度上超过强扩散、优化和RL基线。仅在仿真中训练即可迁移到Unitree G1人形机器人，无需任务特定调参。
Robots in cluttered indoor spaces often fail not because they cannot generate collision-free paths, but because a fixed safety margin is mis-calibrated: conservative margins cause detours and timeouts, while permissive margins lead to near-boundary shortcuts under perception bias. Diffusion-based planners propose diverse trajectory candidates from egocentric RGB-D, yet reliable selection remains the bottleneck. We propose a context-conditioned safety critic that learns an adaptive clearance preference for ranking diffusion proposals, decomposed into three complementary terms: (i) a safety term with a clearance-budget penalty and a control-barrier-function residual for waypoint- and transition-wise safety, (ii) an efficiency term combining a smoothness penalty with a safety-gated detour-ratio penalty that avoids detours without incentivizing risky shortcuts, and (iii) a distance-constraint matching term that anchors the learned budget to realized ESDF clearances to prevent margin collapse. We train the critic with privileged ESDF geometry in simulation and distill it into a perception-only selector via a two-stage teacher-student procedure. On PointGoal navigation in HM3D and MP3D, including cross-dataset transfer, our method achieves the highest success rate (SR) and success weighted by path length (SPL) among strong diffusion, optimization, and RL baselines. Trained purely in simulation, it transfers to a Unitree G1 humanoid and navigates cluttered indoor scenes without task-specific tuning.