UniOMA：最適輸送によるロボット知覚のための統合マルチモーダル構造アライメント
UniOMA: Unified Optimal-Transport Multi-Modal Structural Alignment for Robot Perception
対比損失にGromov-Wasserstein距離ベースの正則化を加え、力覚や触覚を含むモダリティ間の構造的一貫性を高めるマルチモーダル整合法を提案し、5つのロボットベンチマークで性能向上を示した。
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著者: Xinrui Zu, Mark Hoogendoorn, Kevin Sebastian Luck, Shujian Yu
分類: cs.RO, cs.AI
原文アブストラクト
Achieving generalizable and well-aligned multimodal representations remains a core challenge in robot learning. While recent approaches have attempted to align modalities by modeling conditional or higher-order statistical dependencies, they do not explicitly consider 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-modal geometry for each modality and reduce cross-modal structural discrepancies through a dynamically learned GW barycenter, thereby encouraging structural consistency in addition to instance-level contrastive alignment. 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. Extensive experiments on five robotic benchmarks show that our method improves structural alignment and downstream performance over state-of-the-art multimodal contrastive baselines across a wide range of downstream tasks, including robot and environment state prediction. Moreover, our GW-based regularization can be seamlessly integrated into most InfoNCE-like objectives.