Adaptive vs. Static Robot-to-Human Handover: A Study on Orientation and Approach Direction

arXiv cs.RO / 4/27/2026

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Key Points

The study argues that common robot-to-human handover methods use static, open-loop approaches that ignore how humans will grasp the object, forcing users to adapt.
It proposes an adaptive framework that updates the object’s delivery pose in real time using AI-based hand-pose estimation and smooth, kinematics-constrained trajectories.
The system targets both safe approach behavior and improved handover orientation by considering the user’s hand pose and the downstream task.
A user study comparing the adaptive method with a static baseline finds lower cognitive workload and physiological stress and higher perceived trust, using both subjective scales (NASA-TLX, trust) and wearable eye-tracking (blink rate).
The results suggest that task- and pose-aware delivery systems can improve the fluidity and ergonomics of human-robot collaboration.

Abstract

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.