3 accepted papers
Robot manipulation tasks involve direct interactions with objects, which can be viewed as dynamic changes to the robot’s kinematic chain. Morphology-aware learning frameworks, in which robot embodiment is explicitly modeled, do not account for these object-induced changes in their architectures. We …
Contrastive objectives such as InfoNCE align multimodal representations at the instance level but are unable to keep intra-modal geometries, which is called a structural alignment gap. We propose UniOMA, a multimodal structural alignment method using Gromov--Wasserstein (GW) barycenter regularizer t…
Dimensionality reduction (DR) of high-dimensional data is of theoretical and practical interest in machine learning. However, there exist intriguing, non-intuitive discrepancies between the geometry of high- and low-dimensional space. We look into such discrepancies and propose a novel visualization…