Crowd of Oz: A Crowd-Powered Social Robotics System for Stress Management

Abstract

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.

Evaluation of Abstraction Capabilities and Detection of Discomfort with a Newscaster Chatbot for Entertaining Elderly Users

“…State-of-the-art AI and Natural Language Processing (NLP)-boosted conversational assistants can automatically sustain dialogues with end users [ 9 , 10 , 11 ]. Nowadays, chatbots are commonly applied to management [ 12 , 13 , 14 ], education [ 15 , 16 , 17 ] and healthcare [ 18 , 19 , 20 ]. It has been demonstrated that they perform better when designed on purpose for specific domains and user groups, and when their utterances are short and accurate within those domains.…”

Knowledge Triggering, Extraction and Storage via Human-Robot Verbal Interaction

“…Recently, crowdsourcing approaches have been adopted for supporting conversational tasks of social robots, in situations when the robot needed to act as a life coach [37], or assist end-users with information retrieval [38]. However, in these contexts crowd workers recruited in real-time were directly teleoperating the robot's speech, without actually increasing the robot's knowledge base.…”

Smart Environments and Social Robots for Age-Friendly Integrated Care Services

“…The robot is integrated in the context of a robot system named PHAROS which in addition to the robot component it contains a component for human exercises recognition which applies deep learning models on the data recorded by Pepper and a component which recommends physical exercises periodically considering the data from the agenda of the users. In [101] Pepper robot is used in the context of a system called Crowd of Oz (CoZ), an open-source system that allows conversational tasks. The objectives of CoZ are to enhance both the contextual and the social awareness of the workers, to manage the asynchronous nature of the workers during the conversational task and to support the task performance of the workers.…”