LATIDIA · Robótica

Construcción rápida de árboles de la esfera interior mediante aprendizaje automático

arXiv: 2610.23940v1Announce Type: cross Resumen: La detección de colisiones para objetos no convexos complejos es un cuello de botella en la simulación física, la realidad virtual y la robótica. Los árboles de la esfera interior (IST) responden a la proximidad y pe

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arXiv:2610.23940v1 Announce Type: cross Abstract: Collision detection for complex non-convex objects is a bottleneck in physics simulation, virtual reality, and robotics. Inner sphere trees (ISTs) answer proximity and penetration queries efficiently, but their greedy leaf-sphere construction is sequential and computationally expensive, which limits both large-scale preprocessing and runtime use. In this work, we present ML-IST, a machine-learning-based method that reformulates leaf-sphere generation as a single-shot set-prediction problem: a network predicts a fixed set of inscribed sphere candidates from multichannel voxel geometry, differentiable coverage feedback corrects the coarse prediction and fills residual gaps at finer scales, and a lightweight geometric projection enforces validity. Averaged over the test set, a single-GPU implementation