arg-VU：结合物理感知的3D几何进行视觉理解的意图推理
arg-VU: Affordance Reasoning with Physics-Aware 3D Geometry for Visual Understanding in Robotic Surgery
AI总结：
arg-VU通过整合时间一致的几何跟踪与约束诱导的机械建模，提升手术场景中变形组织的意图推理能力，实现更稳定和物理一致的预测。
摘要：
意图推理为感知与行动之间提供了原理性联系，但在手术机器人领域仍待深入探索，因组织高度可变形、顺应性且与工具运动动态耦合。本文提出arg-VU，一种结合物理感知的意图推理框架，整合时间一致的几何跟踪与约束诱导的机械建模，用于手术视觉理解。手术场景通过3D高斯点云（3DGS）重建并转换为时间跟踪的表面表示。扩展位置基于动力学（XPBD）嵌入局部变形约束，生成代表性几何点（RGPs），其约束敏感性定义了各向异性刚度度量，捕捉局部约束-流形几何。机器人工具姿态纳入SE(3)以计算RGPs上的刚性诱导位移，由此推导出两个互补度量：物理感知的顺应能评估机械可行性，以及位置一致性分数捕捉运动对齐（作为运动基线）。实验表明，arg-VU在手术视频数据集上比运动基线更稳定、物理一致且可解释。这些结果表明，物理感知的几何表示能够为变形手术环境提供可靠的意图推理，并支持具身机器人交互。
英文摘要：
Affordance reasoning provides a principled link between perception and action, yet remains underexplored in surgical robotics, where tissues are highly deformable, compliant, and dynamically coupled with tool motion. We present arg-VU, a physics-aware affordance reasoning framework that integrates temporally consistent geometry tracking with constraint-induced mechanical modeling for surgical visual understanding. Surgical scenes are reconstructed using 3D Gaussian Splatting (3DGS) and converted into a temporally tracked surface representation. Extended Position-Based Dynamics (XPBD) embeds local deformation constraints and produces representative geometry points (RGPs) whose constraint sensitivities define anisotropic stiffness metrics capturing the local constraint-manifold geometry. Robotic tool poses in SE(3) are incorporated to compute rigidly induced displacements at RGPs, from which we derive two complementary measures: a physics-aware compliance energy that evaluates mechanical feasibility with respect to local deformation constraints, and a positional agreement score that captures motion alignment (as kinematic motion baseline). Experiments on surgical video datasets show that arg-VU yields more stable, physically consistent, and interpretable affordance predictions than kinematic baselines. These results demonstrate that physics-aware geometric representations enable reliable affordance reasoning for deformable surgical environments and support embodied robotic interaction.
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