Closing the loop in minimally supervised human-robot interaction: formative and summative feedback

Human instructors fluidly communicate with hand gestures, head and body movements, and facial expressions, but robots rarely leverage these complementary cues. A minimally supervised social robot with such skills could help people exercise and learn new activities. Thus, we investigated how nonverbal feedback from a humanoid robot affects human behavior. Inspired by the education literature, we evaluated formative feedback (real-time corrections) and summative feedback (post-task scores) for three distinct tasks: positioning in the room, mimicking the robot's arm pose, and contacting the robot's hands. Twenty-eight adults completed seventy-five 30-s-long trials with no explicit instructions or experimenter help. Motion-capture data analysis shows that both formative and summative feedback from the robot significantly aided user performance. Additionally, formative feedback improved task understanding. These results show the power of nonverbal cues based on human movement and the utility of viewing feedback through formative and summative lenses.

Evaluating speech-in-speech perception via a humanoid robot

Introduction

Underlying mechanisms of speech perception masked by background speakers, a common daily listening condition, are often investigated using various and lengthy psychophysical tests. The presence of a social agent, such as an interactive humanoid NAO robot, may help maintain engagement and attention. However, such robots potentially have limited sound quality or processing speed.

Methods

As a first step toward the use of NAO in psychophysical testing of speech- in-speech perception, we compared normal-hearing young adults' performance when using the standard computer interface to that when using a NAO robot to introduce the test and present all corresponding stimuli. Target sentences were presented with colour and number keywords in the presence of competing masker speech at varying target-to-masker ratios. Sentences were produced by the same speaker, but voice differences between the target and masker were introduced using speech synthesis methods. To assess test performance, speech intelligibility and data collection duration were compared between the computer and NAO setups. Human-robot interaction was assessed using the Negative Attitude Toward Robot Scale (NARS) and quantification of behavioural cues (backchannels).

Results

Speech intelligibility results showed functional similarity between the computer and NAO setups. Data collection durations were longer when using NAO. NARS results showed participants had a relatively positive attitude toward "situations of interactions" with robots prior to the experiment, but otherwise showed neutral attitudes toward the "social influence" of and "emotions in interaction" with robots. The presence of more positive backchannels when using NAO suggest higher engagement with the robot in comparison to the computer.

Discussion

Overall, the study presents the potential of the NAO for presenting speech materials and collecting psychophysical measurements for speech-in-speech perception.

Perceived Safety Assessment of Interactive Motions in Human-Soft Robot Interaction

Soft robots, especially soft robotic hands, possess prominent potential for applications in close proximity and direct contact interaction with humans due to their softness and compliant nature. The safety perception of users during interactions with soft robots plays a crucial role in influencing trust, adaptability, and overall interaction outcomes in human-robot interaction (HRI). Although soft robots have been claimed to be safe for over a decade, research addressing the perceived safety of soft robots still needs to be undertaken. The current safety guidelines for rigid robots in HRI are unsuitable for soft robots. In this paper, we highlight the distinctive safety issues associated with soft robots and propose a framework for evaluating the perceived safety in human-soft robot interaction (HSRI). User experiments were conducted, employing a combination of quantitative and qualitative methods, to assess the perceived safety of 15 interactive motions executed by a soft humanoid robotic hand. We analyzed the characteristics of safe interactive motions, the primary factors influencing user safety assessments, and the impact of motion semantic clarity, user technical acceptance, and risk tolerance level on safety perception. Based on the analyzed characteristics, we summarize vital insights to provide valuable guidelines for designing safe, interactive motions in HSRI. The current results may pave the way for developing future soft machines that can safely interact with humans and their surroundings.

How should robots exercise with people? Robot-mediated exergames win with music, social analogues, and gameplay clarity

Introduction: The modern worldwide trend toward sedentary behavior comes with significant health risks. An accompanying wave of health technologies has tried to encourage physical activity, but these approaches often yield limited use and retention. Due to their unique ability to serve as both a health-promoting technology and a social peer, we propose robots as a game-changing solution for encouraging physical activity. Methods: This article analyzes the eight exergames we previously created for the Rethink Baxter Research Robot in terms of four key components that are grounded in the video-game literature: repetition, pattern matching, music, and social design. We use these four game facets to assess gameplay data from 40 adult users who each experienced the games in balanced random order. Results: In agreement with prior research, our results show that relevant musical cultural references, recognizable social analogues, and gameplay clarity are good strategies for taking an otherwise highly repetitive physical activity and making it engaging and popular among users. Discussion: Others who study socially assistive robots and rehabilitation robotics can benefit from this work by considering the presented design attributes to generate future hypotheses and by using our eight open-source games to pursue follow-up work on social-physical exercise with robots.

Security Aspects of Social Robots in Public Spaces: A Systematic Mapping Study

BACKGROUND

As social robots increasingly integrate into public spaces, comprehending their security implications becomes paramount. This study is conducted amidst the growing use of social robots in public spaces (SRPS), emphasising the necessity for tailored security standards for these unique robotic systems.

METHODS

In this systematic mapping study (SMS), we meticulously review and analyse existing literature from the Web of Science database, following guidelines by Petersen et al. We employ a structured approach to categorise and synthesise literature on SRPS security aspects, including physical safety, data privacy, cybersecurity, and legal/ethical considerations.

RESULTS

Our analysis reveals a significant gap in existing safety standards, originally designed for industrial robots, that need to be revised for SRPS. We propose a thematic framework consolidating essential security guidelines for SRPS, substantiated by evidence from a considerable percentage of the primary studies analysed.

CONCLUSIONS

The study underscores the urgent need for comprehensive, bespoke security standards and frameworks for SRPS. These standards ensure that SRPS operate securely and ethically, respecting individual rights and public safety, while fostering seamless integration into diverse human-centric environments. This work is poised to enhance public trust and acceptance of these robots, offering significant value to developers, policymakers, and the general public.

Hey Robot, Tell It to Me Straight: How Different Service Strategies Affect Human and Robot Service Outcomes

With robots already entering simple service tasks in shops, it is important to understand how robots should perform customer service to increase customer satisfaction. We investigate two methods of customer service we theorize are better suited for robots than human shopkeepers: straight communication and data-driven communication. Along with an additional, more traditional customer service style, we compare these methods of customer service performed by a robot, to a human performing the same service styles in 3 online studies with over 1300 people. We find that while traditional customer service styles are best suited for human shopkeepers, robot shopkeepers using straight or data driven customer service styles increase customer satisfaction, make customers feel more informed, and feel more natural than when a human uses them. Our work highlights the need for investigating robot-specific best practices for customer service, but also for social interaction at large, as simply duplicating typical human-human interaction may not produce the best results for a robot.

Is Politeness Better than Impoliteness? Comparisons of Robot's Encouragement Effects Toward Performance, Moods, and Propagation

This study experimentally compared the effects of encouragement with polite/ impolite attitudes from a robot in a monotonous task from three viewpoints: performance, mood, and propagation. Experiment I investigated encouragement effects on performance and mood. The participants did a monotonous task during which a robot continuously provided polite, neutral, or impolite encouragement. Our experiment results showed that polite and impolite encouragement significantly improved performance more than neutral comments, although there was no significant difference between polite and impolite encouragement. In addition, impolite encouragement caused significantly more negative moods than polite encouragement. Experiment II determined whether the robot's encouragement influenced the participants' encouragement styles. The participants behaved similarly to the robot in Experiment I, i.e., they selected polite, neutral, and impolite encouragements by observing the progress of a monotonous task by a dummy participant. The experiment results, which showed that the robot's encouragement significantly influenced the participants' encouragement styles, suggest that polite encouragement is more advantageous than impolite encouragement.

What Does It Mean to Measure a Smile? Assigning numerical values to emotions

This article looks at the implications of emotion recognition, zooming in on the specific case of the care robot Pepper introduced at a hospital in Toronto. Here, emotion recognition comes with the promise of equipping robots with a less tangible, more emotive set of skills – from companionship to encouragement. Through close analysis of a variety of materials related to emotion detection software – iMotions – we look into two aspects of the technology. First, we investigate the how of emotion detection: what does it mean to detect emotions in practice? Second, we reflect on the question of whose emotions are measured, and what the use of care robots can say about the norms and values shaping care practices today. We argue that care robots and emotion detection can be understood as part of a fragmentation of care work: a process in which care is increasingly being understood as a series of discrete tasks rather than as holistic practice. Finally, we draw attention to the multitude of actors whose needs are addressed by Pepper, even while it is being imagined as a care provider for patients.

Human-Likeness of Feedback Gestures Affects Decision Processes and Subjective Trust

Trust is fundamental in building meaningful social interactions. With the advance of social robotics in collaborative settings, trust in Human–Robot Interaction (HRI) is gaining more and more scientific attention. Indeed, understanding how different factors may affect users’ trust toward robots is of utmost importance. In this study, we focused on two factors related to the robot’s behavior that could modulate trust. In a two-forced choice task where a virtual robot reacted to participants’ performance, we manipulated the human-likeness of the robot’s motion and the valence of the feedback it provided. To measure participant’s subjective level of trust, we used subjective ratings throughout the task as well as a post-task questionnaire, which distinguishes capacity and moral dimensions of trust. We expected the presence of feedback to improve trust toward the robot and human-likeness to strengthen this effect. Interestingly, we observed that humans equally trust the robot in most conditions but distrust it when it shows no social feedback nor human-like behavior. In addition, we only observed a positive correlation between subjective trust ratings and the moral and capacity dimensions of trust when robot was providing feedback during the task. These findings suggest that the presence and human-likeness of feedback behaviors positively modulate trust in HRI and thereby provide important insights for the development of non-verbal communicative behaviors in social robots.

Stop Ignoring Me! On Fighting the Trivialization of Social Robots in Public Spaces

Service and social robot in public scenarios will face various tasks in future applications, such as guiding people or admonishing them to provide assistance or convey social norms. Robots in public spaces might also incorporate roles of authority figures who might admonish people (e.g., security or guard robots). However, recent investigations showed that people ignore the admonishment of robots. Thus, in this work, we are looking at the reasons why people might ignore robots based on the Cognitive Dissonance Theory (CDT). We present the results of two consecutive field observations where a robot admonishes participants (i.e., pedestrians in a shopping mall) and requests them to stop using a smartphone while walking, which is considered an unmoral behavior. In the first field observation, we approached 160 participants over four days and conducted semi-structured interviews with 19 of them. Approximately half of the people ignored the robot, and half of them followed the instructions. Our interview results show that people who ignore the robot indeed use trivialization as a cognitive dissonance reduction strategy to justify ignoring the robot. Based on our analysis of the results, we developed a counter-trivialization strategy that anticipates this dissonance reduction strategy. We admonished 167 participants in our second field observation over four days, and our results show that significantly fewer people ignore the instructions of the robot when the robot uses a counter-trivialization strategy.