3 accepted papers
Manipulating diverse objects with multi-fingered dexterous hands is challenging due to the high dimensionality and complex dynamics. Human-Object Interaction (HOI) datasets provide rich knowledge about task information and embodied interactions. Instead of solely imitating the human demonstrations,
How can robots learn dexterous grasping skills efficiently and apply them adaptively based on user instructions? This work tackles two key challenges: efficient skill acquisition from limited human demonstrations and context-driven skill selection. We introduce AdaDexGrasp, a framework that learns a
Hand-eye calibration plays a fundamental role in robotics by directly influencing the efficiency of critical operations such as manipulation and grasping. In this work, we present a novel framework, EasyHeC++, designed for fully automatic hand-eye calibration. In contrast to previous methods that ne…