仅靠少量触觉信号，就能重建变形物体的完整网格，且不依赖物体形状拓扑。
Topology-Agnostic Mesh Reconstruction of Deformable Objects from Sparse Touch
- 使用统一的注意力架构处理绳索、布料和软体，实现拓扑无关的重建。
- 相比传统方法，重建误差降低约三分之二，触觉点越多优势越明显。
- 通过不确定性指导下一步触碰，尤其在遮挡严重时效果更佳，适合触觉机器人。
在无视觉条件下（如黑暗中、袋内、手遮挡或严重自遮挡），估计可变形物体的完整形状极具挑战。触觉是此类场景下的自然传感器，但触点稀疏且局部。本文提出一种单一的拓扑无关估计器，仅需少数触点即可重建可变形物体的完整网格，无需视觉信息。该方法采用一个排列不变的交叉注意力架构，适用于1D绳索、2D布料和3D体积化软体。所学估计器相较非学习几何补全与高斯过程表面基线，重建误差降低约三分之二；且优于简单全局池化集合编码器，差距随触点增多而增大。进一步表明，估计器的深度集成不确定性可用于指导下一触点位置，显著降低误差，优于随机触碰和高斯过程主动基线，在触点稀疏时表现更优，且在自遮挡严重或误差尾部表现提升明显。当视觉可用时，触点位置影响甚微，凸显了研究纯触觉设定的意义。
原文摘要 · Abstract (English)
Estimating the full shape of a deformable object is especially challenging when vision is unavailable: in the dark, inside an opaque bag, behind the manipulating hand, or under heavy self-occlusion. Touch is the natural sensor in these settings, but touches are sparse and local. We present a single topology-agnostic estimator that reconstructs the full mesh of a deformable object from only a few touches and no vision, using one permutation-invariant cross-attention architecture that handles a 1D rope, a 2D cloth, and a 3D volumetric soft body. The learned estimator reduces reconstruction error by roughly two-thirds relative to non-learned geometric mesh completion and a Gaussian-process surface baseline, and it outperforms a simpler global-pool set encoder, with the gap growing as more touches are observed. We then show that the estimator's deep-ensemble uncertainty can be used to learn where to touch next, which lowers error further and beats both random touching and a Gaussian-process active baseline at sparse budgets. This gain is modest on average but grows with self-occlusion and on the error tail. When vision is also available, where to touch barely matters, motivating the vision-free setting we study.
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