Task-oriented handovers (TOH) are fundamental to effective human-robot collaboration, requiring robots to present objects in a way that supports the human's intended post-handover use. Existing approaches are typically based on object- or task-specific affordances, but their ability to generalize to novel scenarios is limited. To address this gap, we present AFT-Handover, a framework that integrates large language model (LLM)-driven affordance reasoning with efficient texture-based affordance transfer to achieve zero-shot, generalizable TOH. Given a novel object-task pair, the method retrieves a proxy exemplar from a database, establishes part-level correspondences via LLM reasoning, and texturizes affordances for feature-based point cloud transfer. We evaluate AFT-Handover across diverse task-object pairs, showing improved handover success rates and stronger generalization compared to baselines. In a comparative user study, our framework is significantly preferred over the current state-of-the-art, effectively reducing human regrasping before tool use. Finally, we demonstrate TOH on legged manipulators, highlighting the potential of our framework for real-world robot-human handovers.

翻译：面向任务的物体传递（TOH）是实现有效人机协作的基础，要求机器人以支持人类后续使用意图的方式呈现物体。现有方法通常基于物体或任务特定的可供性，但其泛化至新场景的能力有限。为解决这一局限，我们提出了AFT-Handover框架，该框架将大语言模型（LLM）驱动的可供性推理与高效的基于纹理的可供性迁移相结合，实现零样本、可泛化的TOH。给定新的物体-任务组合，该方法从数据库中检索代理样本，通过LLM推理建立部件级对应关系，并对可供性进行纹理化处理以实现基于特征的点云迁移。我们在多种任务-物体组合上评估AFT-Handover，结果显示相较于基线方法，其传递成功率显著提升且具有更强的泛化能力。在对比用户研究中，我们的框架明显优于当前最先进方法，有效减少了人类在使用工具前的重新抓取需求。最后，我们在腿式机械臂上演示了TOH，凸显了该框架在实际机器人-人类物体传递场景中的应用潜力。