DynaFLIP pre-trains dynamics-aware image encoders by aligning image, language, and 3D flow modalities through simplex-volume minimization plus regularizers on video triplets, yielding reusable backbones that improve manipulation policies by up to 22.5% in out-of-distribution settings.
Towards uniformity and alignment for multimodal representation learning.arXiv preprint arXiv:2602.09507
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Adding a dispersion loss plus a bounded cross-modal drift penalty to intermediate embeddings improves unimodal and multimodal accuracy across audio-visual, image-text, and RF benchmarks.
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DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation
DynaFLIP pre-trains dynamics-aware image encoders by aligning image, language, and 3D flow modalities through simplex-volume minimization plus regularizers on video triplets, yielding reusable backbones that improve manipulation policies by up to 22.5% in out-of-distribution settings.
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Diverse via bounded Agreement: Geometric Regularization for Multimodal Fusion
Adding a dispersion loss plus a bounded cross-modal drift penalty to intermediate embeddings improves unimodal and multimodal accuracy across audio-visual, image-text, and RF benchmarks.