Abbas, T.; Khan, V.-J.; Gadiraju, U.; Barakova, E.; Markopoulos, P.: Crowd of oz: A crowd-powered social robotics system for stress management. In: Sensors 20 (2020), Nr. 2, 569. DOI: https://doi.org/10.3390/s20020569
Zusammenfassung: | |
Coping with stress is crucial for a healthy lifestyle. In the past, a great deal of research has been conducted to use socially assistive robots as a therapy to alleviate stress and anxiety related problems. However, building a fully autonomous social robot which can deliver psycho-therapeutic solutions is a very challenging endeavor due to limitations in artificial intelligence (AI). To overcome AI’s limitations, researchers have previously introduced crowdsourcing-based teleoperation methods, which summon the crowd’s input to control a robot’s functions. However, in the context of robotics, such methods have only been used to support the object manipulation, navigational, and training tasks. It is not yet known how to leverage real-time crowdsourcing (RTC) to process complex therapeutic conversational tasks for social robotics. To fill this gap, we developed Crowd of Oz (CoZ), an open-source system that allows Softbank’s Pepper robot to support such conversational tasks. To demonstrate the potential implications of this crowd-powered approach, we investigated how effectively, crowd workers recruited in real-time can teleoperate the robot’s speech, in situations when the robot needs to act as a life coach. We systematically varied the number of workers who simultaneously handle the speech of the robot (N = 1, 2, 4, 8) and investigated the concomitant effects for enabling RTC for social robotics. Additionally, we present Pavilion, a novel and open-source algorithm for managing the workers’ queue so that a required number of workers are engaged or waiting. Based on our findings, we discuss salient parameters that such crowd-powered systems must adhere to, so as to enhance their performance in response latency and dialogue quality. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. | |
Lizenzbestimmungen: | CC BY 4.0 Unported |
Publikationstyp: | Article |
Publikationsstatus: | publishedVersion |
Erstveröffentlichung: | 2020 |
Die Publikation erscheint in Sammlung(en): | Forschungszentren |
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