Title: UniOMA: Unified Optimal-Transport Multi-Modal Structural Alignment for Robot Perception
Primary area: applications to robotics, autonomy, planning
Keywords: robot perception, multimodal alignment, Gromov-Wasserstein distance

Abstract: Achieving generalizable and well-aligned multimodal representation remains a core challenge in artificial intelligence. While recent approaches have attempted to align modalities by modeling conditional or higher-order statistical dependencies, they often fail to capture the structural coherence across modalities.
In this work, we propose a novel multimodal alignment method that augments existing contrastive losses with a geometry-aware Gromov-Wasserstein (GW) distance-based regularization. 
To this end, we encode intra-modality geometry with modality-specific similarity matrices and extend the GW distance to minimize their discrepancies from a dynamically learned barycenter, thereby enforcing structural alignment across modalities beyond what is captured by InfoNCE-like mutual information objectives.
We apply this optimal-transport-based alignment strategy to robot perception tasks involving underexplored modalities such as force and tactile signals, where modality data often exhibit varying sample densities. 
Experimental results show that our method yields superior inter-modal coherence and significantly improves downstream robot perception tasks such as robot and environment state prediction. Moreover, our GW-based augmentation term is versatile and can be seamlessly integrated into most InfoNCE-like objectives.