Visual-Tactile Embodied Perception-Driven Neuro-Symbolic Rule Consistency Modeling and Contact-Deviation Automatic Correction for Precision Assembly of Industrial Humanoid Robots
TL;DR
Experiments show that VTNSCorr achieves the best assembly success rate, rule consistency score, correction success rate, and safety violation reduction, and the results demonstrate its effectiveness for reliable and interpretable industrial humanoid precision assembly.
Abstract
Industrial humanoid robots require robust visual-tactile embodied intelligence to perform precision assembly under occlusion, contact uncertainty, and human-robot coexistence. Existing visuomotor policies and symbolic planning methods often fail to jointly handle continuous contact deviation and discrete industrial rule constraints. This paper proposes VTNS-Corr, a visual-tactile embodied perception-driven neuro-symbolic framework for rule consistency modeling and contact-deviation automatic correction. The method fuses stereo vision, tactile maps, force-torque feedback, proprioception, and human-safety cues into an embodied latent state, grounds it into differentiable symbolic predicates, evaluates fuzzy rule violations, attributes conflict sources, and generates corrective actions such as re-localization, grip adjustment, retreat-and-reinsert, and safety pause. Experiments on RLBench, ManiSkill2, RoboMimic, and DROID show that VTNSCorr achieves the best assembly success rate, rule consistency score, correction success rate, and safety violation reduction. The results demonstrate its effectiveness for reliable and interpretable industrial humanoid precision assembly.