Topology-Agnostic Mesh Reconstruction of Deformable Objects from Sparse Touch

Jul 16, 2026

Researchers have developed a topology-agnostic estimator that reconstructs the full mesh of deformable objects—such as rope, cloth, and soft bodies—using only a few touch inputs and no visual data. The method employs a permutation-invariant cross-attention architecture and achieves a roughly two-thirds reduction in reconstruction error compared to non-learned baselines. Additionally, the approach leverages deep-ensemble uncertainty to guide the selection of subsequent touch points, further improving reconstruction accuracy, especially in challenging scenarios with self-occlusion.

Why it matters: This work advances robotic perception in vision-denied environments, enabling more reliable manipulation of deformable objects using sparse tactile information.

Full story at: arXiv Robotics ↗