← 研踪 PaperTrailarXiv:2611.15339 · v1
Interactive Perception of Granular Materials via Robotic Scooping and Force-Torque Sensing
首次提交 2026-11-11 19:17
版本更新 2026-11-11 19:17
雷达观测 2026-11-13 11:11
摘要
Granular materials exhibit complex and highly variable interaction dynamics, making their reliable manipulation challenging without knowledge of their physical properties. We present an interactive perception framework that infers granular material properties directly from force-torque measurements acquired during scooping. The proposed Latent Filter++ combines a bio-inspired frequency-temporal filter-bank representation with a causal latent dynamical model, allowing the representation to evolve as interaction observations become available while avoiding hand-crafted signal preprocessing. The learned latent representation captures physically meaningful variation in granular materials and supports estimation of properties including grain size and bulk density. Evaluated on 26 diverse granular materials, the approach generalizes to materials excluded from training and enables few-shot adaptation of property-specific mappings using a small number of additional interaction samples. Finally, we demonstrate the relevance of the inferred properties to downstream manipulation by examining their correlation with the maximum acceleration the materials can withstand without spilling from the spoon during transport.
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v1 · 2026-11-11 19:17 · 最新观测版本
Interactive Perception of Granular Materials via Robotic Scooping and Force-Torque Sensing
Granular materials exhibit complex and highly variable interaction dynamics, making their reliable manipulation challenging without knowledge of their physical properties. We present an interactive perception framework that infers granular material properties directly from force-torque measurements acquired during scooping. The proposed Latent Filter++ combines a bio-inspired frequency-temporal filter-bank representation with a causal latent dynamical model, allowing the representation to evolve as interaction observations become available while avoiding hand-crafted signal preprocessing. The learned latent representation captures physically meaningful variation in granular materials and supports estimation of properties including grain size and bulk density. Evaluated on 26 diverse granular materials, the approach generalizes to materials excluded from training and enables few-shot adaptation of property-specific mappings using a small number of additional interaction samples. Finally, we demonstrate the relevance of the inferred properties to downstream manipulation by examining their correlation with the maximum acceleration the materials can withstand without spilling from the spoon during transport.
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