4 accepted papers
We introduce Hand-Objectemph{(HO)GraspFlow}, an affordance-centric approach that retargets a single RGB with hand-object interaction (HOI) into multi-modal executable parallel jaw grasps without explicit geometric priors on target objects. Building on existing learning-based hand reconstruction and …
We propose VISO-Grasp, a novel vision-language-informed system designed to systematically address visibility constraints for grasping in severely occluded environments. By leveraging Foundation Models (FMs) for spatial reasoning and active view planning, our framework constructs and updates an insta
Grasp learning in noisy environments, such as occlusions, sensor noise, and out-of-distribution (OOD) objects, poses significant challenges. Recent learning-based approaches focus primarily on capturing aleatoric uncertainty from inherent data noise. The epistemic uncertainty, which represents the O
Recent advancements in artificial hand development have primarily concentrated on enhancing adaptive grasping, dexterity, as well as the integration of biomimetic skin. However, few designs have successfully combined lightweight, cost-effective solutions, and tactile sensing along with adaptive gras…