A Minimal Optical-Flow Representation for Vision-Based Tactile Rotation Classification in Robotic Manipulation Across Gravity Domains
The study investigates using a compact representation of tactile motion, derived from optical flow between consecutive sensor frames, to classify object rotation under different gravity conditions. A 126-feature representation was used initially, and reducing it to 40 features slightly decreased accuracy (0.963 to 0.957). The key finding is that a single classifier trained across all four gravity domains achieves high accuracy (96.3%) without needing gravity as an input feature.