发表机构
New York University Abu Dhabi（纽约大学阿布扎比分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对室内机器人视觉导航中安全裕度校准问题，提出上下文条件安全评论家方法，通过分解的三个互补项学习自适应间隙偏好排序扩散提议，经训练和提炼后在导航任务中表现出色，还能转移到人形机器人。
AI 中文摘要
在杂乱的室内空间中，机器人失败往往不是因为无法生成无碰撞路径，而是固定安全裕度校准不当。基于扩散的规划器能从自中心RGB-D生成多样轨迹候选，但可靠选择仍是瓶颈。我们提出一种上下文条件安全评论家，学习自适应间隙偏好来对扩散提议排序，分解为三个互补项：安全项、效率项和距离约束匹配项。在模拟中用特权ESDF几何训练评论家，通过两阶段师生过程提炼为仅感知选择器。在PointGoal导航任务中，该方法在强扩散、优化和RL基线中取得最高成功率和成功加权路径长度，还能转移到Unitree G1人形机器人且无需特定任务调整。
英文摘要
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.
