4 accepted papers
The scalability of robotic learning is fundamentally bottlenecked by the significant cost and labor of real-world data collection. While simulated data offers a scalable alternative, it often fails to generalize to the real world due to significant gaps in visual appearance, physical properties, and
A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level grasp stability metrics, neglecting affordance-aware positioning and human-like poses which are crucial for downstream m
Recent advancements in 3D generation models have opened new possibilities for simulating dynamic 3D object movements and customizing behaviors, yet creating this content remains challenging. Current methods often require manual assignment of precise physical properties for simulations or rely on vid…
Cataracts are the leading cause of blindness worldwide, with surgery as the only effective treatment. Accurate prediction of Best Corrected Visual Acuity (BCVA) is crucial for surgical planning. In this paper, we propose a novel Unified Multimodal Learning (UML) framework for BCVA prediction with un…