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Grasp Detection for Robot to Human Handovers Using Capacitive Sensors

Mamaev, Ilshat; Kretsch, David; Alagi, Hosam ORCID iD icon; Hein, Björn


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.

DOI: 10.1109/ICRA48506.2021.9560970
Zitationen: 4
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Fakultät für Informatik – Institut für Prozessrechentechnik, Automation und Robotik (IPR)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 05.2021
Sprache Englisch
Identifikator ISBN: 978-1-7281-9078-5
KITopen-ID: 1000139218
Erschienen in 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 30 May-5 June 2021
Veranstaltung IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, 30.05.2021 – 05.06.2021
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 12552–12558
Nachgewiesen in Dimensions
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