Sensory precision helps autonomous agents resolve conflicting multimodal data
Sensory Precision Inference for Multimodal Arbitration under Uncertainty
Machine LearningNeural and Evolutionary Computing
Summary
Autonomous agents like robots often get unclear or conflicting information from their different sensors, like vision, sound, and touch. The authors developed a model that helps these agents figure out which sensor data to trust more by dynamically estimating how precise or reliable each type of sensory data is. This approach improves the agent’s ability to interpret noisy or missing data and to decide between conflicting sensory signals. They tested their model on a data set that combined images, sounds, and touch data for digits, showing it can better reconstruct and understand the data despite noise or conflicts.
What this means in practice
- •For robotics engineers: Improve robot perception by dynamically weighting sensor reliability for better decision-making under uncertain or conflicting sensor data.
- •For multimodal ai developers: Enhance AI systems that integrate vision, sound, and touch by using inferred sensory precision to achieve robust and interpretable multimodal fusion.
Authors
Tin Mišić, Takato Horii
Abstract
Autonomous agents operating on multisensory data cannot assume that all sensory modalities remain consistently informative. In real environments, sensory streams are frequently corrupted by noise, missing data, or inter-modal incongruence, requiring adaptive arbitration between competing sensory hypotheses. While active inference provides a principled framework for uncertainty-guided inference, the role of dynamically inferred sensory precision in generative multimodal arbitration under sensory conflict remains comparatively underexplored. We propose a multimodal perceptual inference model in which latent beliefs and modality-specific sensory precisions are jointly updated through iterative free-energy minimization. In our proposed model, sensory precision dynamics not only reflect sensory uncertainty but actively shape the evolution of latent beliefs during multimodal conflict. In addition, we introduce a learned prior over sensory precisions that induces structured, class-dependent precision patterns and influences cross-modal inference dynamics. We evaluate the model using a synthetic multimodal MNIST dataset combining visual, auditory, and tactile representations of digit classes under controlled sensory noise and inter-modal incongruence. Results show that dynamic precision inference improves reconstruction robustness under corrupted sensory evidence, supports coherent latent inference from reduced sensory evidence, and enables stable arbitration between conflicting modalities. Furthermore, learned precision priors generate interpretable precision structures that shape inference dynamics and cross-modal latent structure. These findings support sensory precision inference as a mechanistic control process for adaptive multimodal belief formation under uncertainty, highlighting precision dynamics as a computational mechanism for robust and interpretable multisensory integration.