Cross-Modal Visuo-Tactile Object Perception

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• The Cross-Modal Latent Filter (CMLF) improves the efficiency and robustness of latent physical properties estimation under uncertainty compared to baseline approaches.result 90%
• CMLF learns a structured, causal latent state-space of physical object properties.method 95%
• CMLF supports bidirectional transfer of cross-modal priors between vision and touch.technical 90%
• Existing visuo-tactile perception frameworks have limited consideration of how uncertainty and beliefs about object properties evolve over time.limitation 85%
• The model exhibits perceptual coupling phenomena analogous to those observed in humans, including susceptibility to cross-modal illusions.result 80%
• Physical properties like geometry, pose, inertia, stiffness, and contact dynamics are only indirectly observable and cannot always be modeled precisely.technical 90%
• CMLF integrates sensory evidence through a Bayesian inference process that evolves over time.method 90%
• The approach constitutes a significant step toward generalizable, robust and physically consistent cross-modal integration for robotic multi-sensory perception.result 75%

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