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