发表机构
Rice University; Ken Kennedy Institute at Rice University（莱斯大学; 莱斯大学肯·肯尼迪研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基础模型引导的拓扑感知语义风险场，通过方向性权重和测地衰减构建3D成本，用于操作规划，实验表明其能降低语义暴露，超越碰撞避免。
AI 中文摘要
在日常生活环境中的机器人运动规划必须满足硬几何约束，同时考虑上下文相关的语义风险。我们提出了一种基础模型引导的、拓扑感知的语义风险场，将操作安全性扩展到碰撞避免之外。对于每个被操作物体/场景物体对，基础模型提供六个方向性风险权重和一对特定的空间衰减尺度。该方法将这些先验与体素化的3D场景几何相结合，采用拓扑感知的屏蔽和测地空间衰减。一个GPU并行后端批量处理物体级别的距离和风险计算，以构建一个密集的3D场，作为下游运动规划的模块化成本。我们评估了该场在全屏障和部分屏障下的屏蔽行为，并将其3D工作空间表示与像素级语义先验基线进行了比较。在三个家庭模拟场景中，使用所提出场优化的轨迹在相同几何约束下比仅碰撞避免的轨迹具有更低的语义暴露。我们还评估了支持管道的计算实用性和可靠性。这些结果共同支持所提出的场作为超越碰撞避免的操作规划的一种实用拓扑感知语义成本表示。
英文摘要
Robot motion planning in everyday environments must satisfy hard geometric constraints while accounting for context-dependent semantic risk. We present a foundation-model-guided, topology-aware semantic risk field that extends manipulation safety beyond collision avoidance. For each manipulated-object/scene-object pair, a foundation model provides six directional risk weights and a pair-specific spatial decay scale. The method combines these priors with voxelized 3D scene geometry using topology-aware shielding and geodesic spatial decay. A GPU-parallel backend batches object-level distance and risk computations to construct a dense 3D field that serves as a modular cost for downstream motion planning. We evaluate the field's shielding behavior under full and partial barriers and compare its 3D workspace representation with a pixel-wise semantic-prior baseline. Across three household simulation scenarios, trajectories optimized with the proposed field have lower semantic exposure than collision-only trajectories under the same geometric constraints. We also evaluate the computational practicality and reliability of the supporting pipeline. Together, these results support the proposed field as a practical topology-aware semantic cost representation for manipulation planning beyond collision avoidance.
