Task-Oriented Robot-Human Handovers on Legged Manipulators

We present AFT-Handover, a framework that integrates large language model (LLM)-driven affordance reasoning with texture-based affordance transfer to achieve zero-shot, task-oriented robot-to-human handovers. In a comparative user study, our framework is preferred over the current state-of-the-art by 71.43% of the participants, reducing human regrasping effort and enhancing perceived task understanding. We demonstrate real-world task-oriented handovers on legged manipulators, highlighting the potential of integrating semantic reasoning with affordance transfer for robot-human handovers on mobile manipulators. For more information, check out our paper (https://arxiv.org/abs/2602.05760) or come visit our talk at the International Conference on Human-Robot Interaction (HRI 2026)! Authors: Andreea Tulbure, Carmen Scheidemann, Elias Steiner, Marco Hutter — Robotic Systems Lab, ETH Zürich