UniOMA: Unified Optimal-Transport Multi-Modal Structural Alignment for Robot Perception
Overview
This paper proposes UniOMA, a multimodal alignment method that augments contrastive losses with geometry-aware Gromov-Wasserstein distance regularization to ensure structural consistency across modalities. The approach is applied to robot perception tasks involving force and tactile signals with varying sample densities. Experiments on five robotic benchmarks demonstrate improved structural alignment and downstream performance compared to state-of-the-art multimodal contrastive baselines, with seamless integration into InfoNCE-like objectives.
Generated from attributed reports · Updated 6 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalUniOMA: Unified Optimal-Transport Multi-Modal Structural Alignment for Robot Perception
This paper proposes UniOMA, a multimodal alignment method that augments contrastive losses with geometry-aware Gromov-Wasserstein distance regularization to ensure structural consistency across modalities. The approach is applied to robot perception tasks involving force and tactile signals with varying sample densities. Experiments on five robotic benchmarks demonstrate improved structural alignment and downstream performance compared to state-of-the-art multimodal contrastive baselines, with seamless integration into InfoNCE-like objectives.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).