Receiver-Centered Robot-to-Human Handover with Grasp-Aware Object Orientation

Abstract:Collaborative robots are increasingly sharing workspaces with human operators, making tool handover a frequent and safety-critical micro-interaction. However, traditional static handovers often lead to awkward grasps when handling asymmetric industrial tools. This paper presents a receiver-centered voice-driven adaptive handover system for mechanical tools, built on a Franka cobot. Using an LLM for intention recognition and MediaPipe for real-time 3D hand tracking, the framework dynamically adjusts the end-effector's orientation to present tools in an ergonomically optimal, handle-first pose. A within-subjects study compared this adaptive approach with an object-agnostic static baseline. The results demonstrate that the adaptive system reduces the grasp delay for asymmetric tools, improving the fluency of the interaction. Furthermore, the adaptive strategy improved specific trust-related perceptions, particularly motion predictability and perceived task simplicity.

Accepted for presentation at the 19th International Workshop on Human-Friendly Robotics (HFR 2026), Trento, Italy. The paper will appear in Springer's Proceedings in Advanced Robotics

Robotics (cs.RO)

arXiv:2607.17839 [cs.RO]

(or arXiv:2607.17839v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.17839 arXiv-issued DOI via DataCite (pending registration)