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

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Adaptive vs. Static Robot-to-Human Handover: A Study on Orientation and Approach Direction Federico Biagi, Dario Onfiani, Simone Silenzi, Cristina Iani, Luigi Biagiotti submitted to IEEE RO-MAN 2026

Overview

This work presents a novel adaptive handover framework that dynamically adjusts the object's delivery pose in real-time based on the user's hand position and orientation, while aligning the object according to the specific downstream task. The system combines AI-based hand pose estimation (MediaPipe and FrankMocap) with smooth Bézier curve trajectories, parameterized by a limited-jerk fifth-order polynomial. A comprehensive user study with 14 participants compares the adaptive approach against a static baseline using both subjective metrics (NASA-TLX, Human-Robot Trust Scale) and objective physiological data (blink rate from wearable eye-trackers).

Key Contributions

• Dynamic Kinematic Control: Real-time position and orientation adaptation using AI pose estimation, cubic Bézier trajectories, and SLERP interpolation.
• Comparative Experimental Validation: Multimodal evaluation combining subjective questionnaires with objective physiological measurements (eye-tracking blink rate).

Repository Structure

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├── index.html              # Project page (GitHub Pages)
└── static/                 # Website assets
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    └── images/             # Converted PNG figures

Acknowledgments

This work was partially supported by:

• The Italian National Recovery and Resilience Plan (PNRR), PRIN Project I-SHARM: Intelligent SHared Autonomy for Robotic Manipulation Systems (Project ID 2022NTZRFM)
• The University of Modena and Reggio Emilia FAR project ROBIN3

Website License

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