Robot-to-human handovers often rely on static, open-loop strategies (or, at best, approaches that adapt only the position), which generally do not consider how the object will be grasped by the human, thus requiring the user to adapt. This work presents a novel adaptive framework that dynamically adjusts the object's delivery pose in real time based on the user's hand pose and the intended downstream task. By integrating AI-based hand pose estimation with smooth, kinematically constrained trajectories, the system ensures a safe approach and an optimal handover orientation. A comprehensive user study compares the proposed adaptive approach against a static baseline across multiple tasks, evaluating both subjective metrics (NASA-TLX, Human-Robot Trust Scale) and objective physiological data (blink rate measured via wearable eye-trackers). The results demonstrate that dynamic alignment significantly reduces users' cognitive workload and physiological stress, while increasing perceived trust in the robot's reliability. These findings highlight the potential of task- and pose-aware systems for enabling fluid and ergonomic human-robot collaboration.

翻译：机器人-人手交接常依赖静态开环策略（或至多仅调整位置的方法），一般未考虑人类将如何抓取物体，因而需要用户进行适应。本文提出一种新型自适应框架，基于用户手部姿态及预期下游任务动态调整物体的递送位姿。通过将基于人工智能的手部姿态估计与平滑且受运动学约束的轨迹相结合，该系统确保安全接近与最优交接朝向。一项综合用户研究将所提出的自适应方法与静态基准在多任务情境下进行对比，评估了主观指标（NASA-TLX量表、人机信任量表）与客观生理数据（通过可穿戴眼动仪测量的眨眼频率）。结果表明，动态对齐显著降低了用户的认知负荷与生理压力，同时增强了对机器人可靠性的感知信任。这些发现凸显了面向任务与姿态感知系统在促进顺畅且符合人体工学的协作中的潜力。