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

2026-04-24

AI Summary

This work proposes a real-time adaptive object handover framework that addresses the limitations of traditional robotic handover methods, which typically rely on static or position-only adaptive strategies and neglect the user’s grasping posture and downstream task requirements, resulting in unnatural human–robot interaction. By integrating hand pose estimation with task intent awareness for the first time, the framework dynamically optimizes delivery poses through AI-driven gesture recognition, smooth motion planning, and kinematically constrained trajectory generation. This approach enhances ergonomic compatibility while ensuring safety. User studies demonstrate that, compared to baseline methods, the proposed framework significantly reduces cognitive load and physiological stress—measured via blink rate—and increases users’ trust in the robot’s reliability, thereby improving the fluency and naturalness of human–robot collaboration.

Technology Category

Intelligent Robots: Human-Robot InteractionHumans and AI: Interaction Techniques and DevicesCognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomyUser Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating successEconomics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• robot-to-human handover
• adaptive handover
• hand pose estimation
• human-robot collaboration
• cognitive workload

Innovation

• adaptive handover
• hand pose estimation
• human-robot collaboration
• cognitive workload
• kinematically constrained trajectory