Self-supervised prediction of the intention to interact with a service robot

Author:

Abbate GabrieleORCID,Giusti AlessandroORCID,Schmuck ViktorORCID,Celiktutan OyaORCID,Paolillo AntonioORCID

Funder

Staatssekretariat für Bildung, Forschung und Innovation

Horizon 2020

European Commission

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Elsevier BV

Subject

Computer Science Applications,General Mathematics,Software,Control and Systems Engineering

Reference49 articles.

1. How to compete with robots by assessing job automation risks and resilient alternatives;Paolillo;Science Robotics,2022

2. Receptionist or information kiosk: how do people talk with a robot?;Lee,2010

3. Applications and implications of service robots in hospitality;Tuomi;Cornell Hosp. Q.,2021

4. A survey on the application trends of home service robotics;Zachiotis,2018

5. Navigation assistance and guidance of older adults across complex public spaces: the DALi approach;Palopoli;Intell. Serv. Robot.,2015

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1. Beyond Supervised: The Rise of Self-Supervised Learning in Autonomous Systems;Information;2024-08-16

2. Predicting the Intention to Interact with a Service Robot: the Role of Gaze Cues;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. The Discontent with Intent Estimation In-the-Wild: The Case for Unrealized Intentions;Extended Abstracts of the CHI Conference on Human Factors in Computing Systems;2024-05-11

4. A Long-Range Mutual Gaze Detector for HRI;Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction;2024-03-11

5. Introducing Social Robots to Assess Frailty in Older Adults;Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction;2024-03-11

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