Grasp Detection for Robot to Human Handovers Using Capacitive Sensors
Ilshat Mamaev, David Kretsch, Hosam Alagi, Björn Hein
Abstract
As it happens, despite yet unmatched by robots perception and motor skills humans drop objects during handover because of false grasp detection and early release. Accordingly, the fluent robot-human handover is still an open challenge. This paper presents an approach to a natural robot to human handover using Capacitive Proximity Sensor (CPS) for robust grasp detection and release trigger. We propose an experimental setup for the evaluation using a collaborative robot, an eye-in-hand depth camera, and CPS integrated into the gripper. Three grasp detection methods were implemented and an object release was triggered based on torque-sensing, capacitive sensing, and the combination of both. Finally, a user study was designed and conducted, indicating that the capacitive method is the most preferred type with the shortest human idle time and the highest fluency ratings.
BibTeX
@inproceedings{icra2021_graspdetectionfo,
title = {Grasp Detection for Robot to Human Handovers Using Capacitive Sensors},
author = {Ilshat Mamaev and David Kretsch and Hosam Alagi and Björn Hein},
booktitle = {ICRA 2021},
year = {2021}
}