SOURCE-LINKED INTELLIGENCE

Hand-Aware Transition Modeling for Bimanual Procedural Anomaly Detection

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

Procedural anomaly detection in bimanual assembly requires judging each hand action against the execution so far. A corrective action may look unusual in isolation, while a visually plausible action can violate the order of the procedure. We present HACT, a transition model over predicted per-hand events. A role-preserving history keeps the concurrent responsibilities of both hands, and a marked temporal point process assigns each observed transition a semantic and temporal surprisal. A supervised evidence head and a two-state filter convert these surprisals into per-hand anomaly posteriors. A

Read original source ↗ Open in workspace

recordType

paper

region

Global