Crowd-Powered Conversational Systems (CPCS) are gaining traction due to their potential utility in a range of application fields where automated conversational interfaces are still inadequate. Currently, long response times negatively impact CPCSs, limiting their potential application as conversational partners. Related research has focused on developing algorithms for swiftly hiring workers and synchronous crowd coordination techniques to ensure high-quality work. Evaluation studies typically concern system reaction times and performance measurements, but have so far not examined the effects of extended wait times on users. The goal of this study, based on time perception models, is to explore how effective different time fillers are at reducing the negative impacts of waiting in CPCSs. To this end, we conducted a rigorous simulation-based between subjects (N = 930) study on the Prolific crowdsourcing platform to assess the influence of different filler types across three levels of delay (8, 16 & 32s) for Information Retrieval (IR) and stress management tasks. Our results show that asking users to perform secondary tasks (e.g., micro tasks or breathing exercises) while waiting for longer periods of time helped divert their attention away from timekeeping, increased their engagement, and resulted in shorter perceived waiting times. For shorter delays, conversational fillers generated more intense immersion and contributed to shorten the perception of time.

The aim of this study is to understand users' experience and their perceived privacy, while interacting with a crowd-operated social robot. We conducted a between-subjects user study, wherein the robot broadcasts both audio and video to crowd workers in one condition, as opposed to broadcasting only the participants' audio cues in the other condition. A sample of 14 students took part in this study, and was divided into 2 groups (video and No-Video group). Participants were asked to use the help of a crowd-operated Pepper robot to find their next holiday destination. Once the interaction was completed, participants assessed the social intelligence, user experience and privacy aspects of the robot in both conditions. No significant differences were experienced by participants regarding social intelligence and user experience across both conditions. Interestingly, less privacy was perceived by the group with an audio-only broadcast feed compared to the audio-video feed.

We present Crowd of Oz (CoZ) - a crowd powered system to enable real time dialogue between a Pepper robot and a user. CoZ includes media rich interfaces and advanced architecture to swiftly elicit crowd responses for the Pepper robot. We have empirically demonstrated that users can be engaged in a beneficial discussion with the crowd-powered robot, that acts as a coach to mitigate stress.

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.

On-demand emotional support is an expensive and elu- sive societal need that is exacerbated in difficult times – as witnessed during the COVID-19 pandemic. Prior work in affective crowdsourcing has examined ways to overcome technical challenges for providing on-demand emotional support to end users. This can be achieved by training crowd workers to provide thought- ful and engaging on-demand emotional support. Inspired by recent advances in conversational user interface research, we investigate the efficacy of a conversational user interface for training workers to deliver psychological support to users in need. To this end, we conducted a between-subjects experimental study on Prolific, wherein a group of workers (N=200) received training on motivational interviewing via either a conversational interface or a conventional web interface. Our results indicate that training workers in a conversational interface yields both better worker performance and improves their user experience in on-demand stress management tasks.