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With recent technological improvements, the performance of conversational robots such as context-aware dialogue is expected to be dramatically enhanced. While context-aware utterance is considered one of the crucial functions of conversational robots, only a few studies investigate the relationship between context awareness and user preference.  As is the case with human social relationships, human-robot interactions are under the effect of user&#39;s attributes such as personality, and thus considering these factors of users is essential to continue interacting with robots. In this paper, we evaluate the preference for context awareness based on personality trait effects in an open-domain dialogue using a conversational android robot system. Two types of utterances, context-aware and context-free, are implemented in the robot system, and the participants talk with and evaluate our conversational system. The preference for two systems and the personality traits of each participant are collected. We analyze the relationship between preferences and personalities. The results suggest that the participants&#39; preference for whether an utterance is context-aware or context-free is distinguished by some personality traits.

IEEE RO-MAN 2024

Evaluation of Preference on Context-Aware Utterances Based on Personality Traits Using a Conversational Android Robot System

context-aware dialogue

android robots

personality

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With recent technological improvements, the performance of conversational robots such as context-aware dialogue is expected to be dramatically enhanced. While context-aware utterance is considered one of the crucial functions of conversational robots, only a few studies investigate the relationship between context awareness and user preference.  As is the case with human social relationships, human-robot interactions are under the effect of user's attributes such as personality, and thus considering these factors of users is essential to continue interacting with robots. In this paper, we evaluate the preference for context awareness based on personality trait effects in an open-domain dialogue using a conversational android robot system. Two types of utterances, context-aware and context-free, are implemented in the robot system, and the participants talk with and evaluate our conversational system. The preference for two systems and the personality traits of each participant are collected. We analyze the relationship between preferences and personalities. The results suggest that the participants' preference for whether an utterance is context-aware or context-free is distinguished by some personality traits.

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The 33rd IEEE International Conference on Robot and Human Interactive Communication was held in Pasadena, California, USA. The theme of the IEEE RO-MAN 2024 was “Embracing Human-Centered HRI”.

Robots deployed into real-world task-based environments may need to provide assistance, troubleshooting, and on-the-fly instruction for human users. While previous work has considered how robots can provide this assistance while co-located with human teammates, it is unclear how robots might best support users once they are no longer co-located. We propose the use of Augmented Reality as a medium for conveying long-distance task guidance from humans existing robot teammates, through Augmented Reality facilitated Robotic Guidance (ARRoG). Moreover, because there are multiple ways that a robot might project its identity through an Augmented Reality Head Mounted Display, we identify two candidate designs inspired by existing interaction patterns in the human-robot interaction (HRI) literature (re-embodiment-based and telepresence-based identity projection designs), present the results of a design workshop to explore how these designs might be most effectively implemented, and the results of a human-subject study intended to validate these designs

Designing Augmented Reality Robot Guidance Interactions through the Metaphors of Re-Embodiment and Telepresence

This work describes an immersive control system for a quadruped robot, designed to track the head movements of the operator wearing a virtual reality eye-wear, while also utilizing joystick commands for locomotion control. The article details the implemented closed-loop velocity control approach and the locomotion task specifications. The proposed method has been implemented on a Spot robot from Boston Dynamics, with Meta Quest 2 virtual reality system. Evaluation of the approach involved a user study, where participants engaged in immersive control of the quadruped robot within an indoor experimental environment and provided feedback through standardized questionnaires. Pairwise comparison of the resulting data revealed significant advantages for the proposed immersive control system over a standard remote controller, with enhanced performance observed in the second trial of using the control system. However, participants lacking experience with virtual reality systems reported increased distress symptoms following the experiment.

Immersive Control of a Quadruped Robot with Virtual Reality Eye-Wear

Understanding human emotions is paramount for effective human-human interactions. As technology advances, social robots are increasingly being developed with the capability to discern and respond to human emotions, with the ultimate aim of providing assistance and companionship. However, existing research on emotion recognition for human-robot interaction predominantly focuses on facial expressions or verbal speech, neglecting other potential mediums of emotional expression. In this study, we shed light on the significance of considering various forms of emotional expression, mainly non-verbal vocalization known as vocal bursts, which have been overlooked in the context of emotion modeling for human-robot interaction. Vocal bursts, characterized by brief and intense vocal utterances, represent a rich source of emotional cues that can significantly enhance the capabilities of social robots in understanding and responding to human emotions. Driven by the increasing interest in vocal bursts within speech and affective computing research, we propose a baseline model for affective vocal burst recognition that can outperform large audio models. The proposed baseline model achieves weighted F1 scores of 0.606, 0.342, and 0.287 on 10, 24, and 30 emotion classes, respectively. Additionally, we identify challenges that must be addressed to enhance affective vocal burst recognition for human-robot interaction. Code available at github.com/rahullabs/Sigh

Sigh!!! There Is More Than Just Faces and Verbal Speech to Recognize Emotion in Human-Robot Interaction

Telepresence robots can enhance communication experiences by providing a sense of physical presence, embodiment and may evoke co-presence. In spite of that, telepresence robots have not made it fully to consumer markets. In this paper, we investigate how different levels of controlling a telepresence robot (teleoperation, shared control, and no control) influence presence. To this aim, we conducted a study (N=45) where participants were evenly distributed to one of the robot control conditions. The task involved navigating an unknown room and listening to stories told by a person co-located with the robot. We collected subjective impressions of presence using the temple presence inventory and performed a thematic content analysis on a post-experiment interview. Our results suggest nuances in perceived presence under different levels of robot control after performing a thematic content analysis. Co-presence can be experienced during teleoperation and shared control, and teleoperation may evoke negative sentiments if it does not provide enough spatial information during navigation. However, our results did not point to significant differences in spatial or social presence. We consider that these findings encourage further discussions on how presence is perceived in robot-mediated communication.

An Exploratory Study on the Impact of Varying Levels of Robot Control on Presence in Robot-Mediated Communication

Looking at state-of-the-art robots, the majority have a white surface. This raises the questions of why robots are designed predominantly white, and how surface color affects the robots social perception. This not only reinforces human stereotypes but also influences the acceptance and usage of robots. Accordingly, this online study with a 2 (black vs. white) x 3 (Pepper robot vs. Temi robot vs. Kuka robot) within-subjects design (N = 100) investigated the effect on racist stereotypical perception and the role of anthropomorphism using implicit (Implicit Association Test) and explicit (Robotic Social Attributes Scale) measures and questionnaires on behavioral intentions. Our results show that robot color influences the implicit perception of robots but rarely affects explicit perception and behavior measures. Moreover, no effects of the robots' anthropomorphism on racist perceptions were found. As more effects of anthropomorphism on our outcome variables were found than for robot color, we assume that robot color activates stereotypes in terms of color rather than race.

All Too White? Effects of Anthropomorphism on the Stereotypical Perception of Robot Color

The rapid advancements of drones have demonstrated the versatility and promising potential of human-swarm systems (HSS) across various domains. However, human performance within these systems may be impaired by factors such as limited domain knowledge and mental stress, often leading to cognitive overload and hindering the efficiency and effectiveness of human-swarm teaming. Consequently, the accurate monitoring of cognitive load levels is crucial for optimizing HSS performance. To address the challenges of existing measurement methods, which are often expensive, time-consuming, or lack real-time capabilities, we propose a Large Language Model driven cognitive load prediction framework. This framework integrates comprehensive task context, domain knowledge, and behavior analysis to provide fast and cost-effective predictions in complex scenarios. By leveraging the capabilities of Large Language Models and employing reinforcement learning to model the cognitive load generation, our framework aims to offer real-time insights into human-related factors causing high cognitive load and predict cognitive levels over time, ultimately enhancing the performance of HSS teaming.

Large Language Model Driven Interactive Learning for Real-Time Cognitive Load Prediction in Human-Swarm Systems

Mobile robots are being introduced to industrial workplaces in roles that require copresence with humans. To develop effective robots that do not negatively impact humans, including their subjective experience and ability to get their work done, we must understand humans needs for working near these robots. In this paper, we examine what human workers need from copresent robots motion during work at a large warehouse. To do so, report and synthesize findings about robot motion from across five studies (e.g., focus group, observation, experiment). Results indicate that workers were most focused on robot movement, including consistency, distance, prioritizing people, and indicating when it sensed people. Researchers and practitioners can use these findings to prioritize what aspects of mobile robots to develop to improve human worker experiences around robots and team efficiency.

But Where Are You Going?! Motion Is What Is Most Important for Real-World Co-Present Mobile Robots

In real-world scenarios, robots come closer to humans in many applications, sharing the same workspace or even manipulating the same objects. To ensure safe and intuitive collaboration, it is crucial to have an accurate knowledge of the human's 3D position in space, which should be estimated with high precision, high frequency and low latency. However, individual sensors such as inertial measurement units (IMUs) or cameras cannot meet all requirements for reliable human pose estimation under conditions such as long operating times, large distances and occlusions. In this study, we highlight the limitations of different visual pose methods and present a fused approach for real-time estimation of the 3D position of the human pelvis using machine learning-based visual pose from a monocular camera and an IMU sensor suit. The multimodal fusion is based on the Invariant Extended Kalman filter (InEKF) on Lie Groups, which fuses drift-free visual poses with high-frequency inertial measurements in a loosely-coupled manner. The evaluation is performed on a recorded dataset of multiple subjects performing various experimental scenarios. The results show that the fused approach can increase the accuracy and robustness of the estimates, taking a step closer towards smooth human-robot collaboration.

Fusion of Inertial Sensor Suit and Monocular Camera for 3D Human Pelvis Pose Estimation

Frailty assessment plays a pivotal role in providing older adults care. However, the current process is time-consuming and only measures patients' completion time for each test. This paper introduces a set of algorithms to be used in robots to autonomously perform frailty assessments. In doing so we aim at reducing therapists' burden and provide additional frailty-related metrics that can enhance the effectiveness of diagnosis. We conducted a pilot study with 22 elderly participants and compared our system's performance with that of medical professionals to assess its precision. The results demonstrate that our approach achieved performances close to that of its human counterpart. This research represents an important step forward in the integration of social robotics in healthcare, offering potential benefits for patient care and clinical decision-making.

Exploring the Potential of a Robot-Assisted Frailty Assessment System for Elderly Care

This study explores the complex dynamics between humans and robots, focusing on how emotional states influence proxemics. We conducted a user study using a standard mobile robot to investigate whether emotions elicited from a loudspeaker, affect human perception of robot proximity. Based on previous research on Human-Human Interaction (HHI), we analysed participants responses to robots displaying different behaviours. Participants observed the robots approach while experiencing positive or negative emotions. Our findings suggest that emotional states induced by external stimuli can affect participants perception of robot proximity. In detail, the results indicate that while comfortable stopping distances were unaffected by participants emotional state, individuals who experienced positive emotions judged the same proxemics distance used while performing an avoidance behaviour to be more acceptable compared to the case of negative emotions. This study describes the extent to which our emotions can alter the perception of robot behaviours, ultimately affecting our acceptance of these novel social agents.

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Designing Augmented Reality Robot Guidance Interactions through the Metaphors of Re-Embodiment and Telepresence

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