LLM-Handover:Exploiting LLMs for Task-Oriented Robot-Human Handovers

Abstract--Effective human-robot collaboration depends on task-oriented handovers, where robots present objects in ways that support the partner's intended use. T o address this gap, we propose LLM-Handover, a novel framework that integrates large language model (LLM)-based reasoning with part segmentation to enable context-aware grasp selection and execution. Given an RGB-D image and a task description, our system infers relevant object parts and selects grasps that optimize post-handover usability. T o support evaluation, we introduce a new dataset of 60 household objects spanning 12 categories, each annotated with detailed part labels. We first demonstrate that our approach improves the performance of the used state-of-the-art part segmentation method, in the context of robot-human handovers. Next, we show that LLM-Handover achieves higher grasp success rates and adapts better to post-handover task constraints. During hardware experiments, we achieve a success rate of 83% in a zero-shot setting over conventional and unconventional post-handover tasks. Finally, our user study underlines that our method enables more intuitive, context-aware handovers, with participants preferring it in 86% of cases. S robots become more common in everyday settings, their ability to collaborate with humans on joint tasks becomes increasingly important. Recent research in human-robot interaction explores these challenges, with object handovers being a key component for successful collaboration [1]. These handovers form the basis for many joint activities that require both physical coordination and contextual understanding [2]. For instance, observations of human-to-human handovers reveal that people often anticipate each other's intended use of an object by interpreting the surrounding context [2], [3]. This ability, known as task-orientation, becomes especially critical in environments like factories, surgeries, or construction sites, where one partner may have limited mobility or freedom to adjust their actions.