PGA: Personalizing Grasping Agents with Single Human-Robot Interaction

arXiv:2310.12547v2 Announce Type: replace-cross
Abstract: Language-Conditioned Robotic Grasping (LCRG) aims to develop robots that comprehend and grasp objects based on natural language instructions. While the ability to understand personal objects like my wallet facilitates more natural interaction with human users, current LCRG systems only allow generic language instructions, e.g., the black-colored wallet next to the laptop. To this end, we introduce a task scenario GraspMine alongside a novel dataset aimed at pinpointing and grasping personal objects given personal indicators via …

agents arxiv cs.cv cs.lg cs.ro grasping human robot type