United States

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&#39; 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.

IEEE RO-MAN 2024

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

stereotypical perception

robot color

behavioral intentions

anthropomorphism

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

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.

technical paper

[![](https://assets.underline.io/markdown_image/1/image/f25361a85b7c83a7476c0b3c1c0bb1b2.png)](https://drive.google.com/file/d/18jcAcciCBQHj6R07Dbpm2HCOBEIwAOCU/view?usp=sharing)


Dear Colleagues and Friends,

The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024, was held at Pasadena Convention Center in Pasadena, USA.

The theme of the IEEE RO-MAN 2024 was **“Embracing Human-Centered HRI”**.

With the continuous technological effort of understanding human needs, creating human-desired robotic systems, and developing real human user experiences, we are marching together towards building a promising and excellent human quality of life, all for our wellness and our next generation.

The conference provided a forum for researchers and practitioners to discuss a variety of topics related to the broad theme of Robot and Human Interactive Communication involving theories, methodologies, technologies, empirical and experimental studies, and generated more insights for the future of Robot and Human Interactive Communication!

We hope you continue to enjoy the post-conference content on Underline!
<br><br>

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

It looks like you are not registered for this event. 

A registration is required to access this event page. 



Registration Is Required

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”.

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.

Are Emotions Important? a Study on Social Distances for Path Planning Based on Emotions

The development of social robots with advanced conversational capabilities aiming to engage humans in natural interactions has recently surged. This paper investigates the dynamic aspect of Human-Robot Interaction (HRI), focusing on the regulation of interpersonal distance based on human emotion. Through a user study with a humanoid robot, we explore how participants perceive and respond to rule-based versus randomly generated robot behaviours in adjusting interpersonal space during an unconstrained conversation. Results suggest that participants perceive the robot using rule-based behaviours as more socially competent and adaptable to human behaviour and emotions compared to the random ones. These findings highlight the importance of considering subtle nonverbal cues and adapting robot behaviour based on human emotions to improve the quality of HRI, and consequently facilitate the successful integration of human natural nuances in robots.

Too Close to You? a Study on Emotion-Adapted Proxemics Behaviours

Complex manipulation tasks often require robots with complementary capabilities to collaborate. We introduce a benchmark for LanguagE-Conditioned Multi-robot MAnipulation (LEMMA) focused on task allocation and long-horizon object manipulation based on human language instructions in a tabletop setting. LEMMA features 8 types of procedurally generated tasks with varying degree of complexity, some of which require the robots to use tools and pass tools to each other. For each task, we provide 800 expert demonstrations and human instructions for training and evaluations. LEMMA poses greater challenges compared to existing benchmarks, as it requires the system to identify each manipulators limitations and assign sub-tasks accordingly while also handling strong temporal dependencies in each task. To address these challenges, we propose a modular hierarchical planning approach as a baseline. Our results highlight the potential of LEMMA for developing future language-conditioned multi-robot systems.

LEMMA: Learning Language-Conditioned Multi-Robot Manipulation

Recent advancements in medical science and technology have led to a remarkable increase in the lifespan of the global elderly population. However, this demographic often struggles with mobility issues, leading to a sedentary lifestyle fueled by concerns over their physical capabilities. Traditional treadmill gait training, although beneficial, often becomes monotonous and lacks the real-world feedback necessary for engaging and effective rehabilitation. Addressing this gap, research into virtual walking systems utilizing avatar robots has gained traction. Despite the progress, several challenges remain where the systems prioritize visual feedback without considering the crucial need for alerting users to potential dangers and obstacles. This lack of comprehensive environmental awareness and feedback undermines both user engagement and safety. To address this problem, this paper proposes an algorithm that employs B-splines for precise free space detection, integrated with a safety stop mechanism for an avatar robot. This novel approach enhances user awareness of their surroundings through a sophisticated Graphic User Interface (GUI) that leverages Augmented Reality (AR) technology. By superimposing free space boundaries and warning messages directly onto a real-time camera feed, the system provides an intuitive and immersive navigation aid. The efficacy of our proposed GUI was rigorously tested across a series of realistic scenarios, comparing teleoperation control performance with and without the augmented interface. Our findings reveal that our GUI markedly enhances user safety and navigational effectiveness, fostering a deeper understanding of and interaction with the surrounding environment, thereby redefining user experience in virtual mobility assistance.

A B-Spline Approach for Improved Environmental Awareness in Virtual Walking System Using Avatar Robot

Research on underwater human-robot teaming is particularly promising because of complementary sensory skills that may overcome unique challenges associated with the combination of limited connectivity and low visibility. Nevertheless, testing human-robot collaboration under water, especially in complex, real-world scenarios, poses severe safety, cost, and time constraints that significantly hinder academic research in this space. This paper presents a novel cyber-physical extended-reality (XR) testbed, referred to as Underwater Real-Time Human Autonomous Systems Collaboration (UnRealTHASC), designed to enable human-robot interactions in simulated photorealistic environments with mixed real and virtual wearables, and advanced autonomous underwater sensors in-the-loop. Novel sensor interfaces are designed to integrate real and virtual sensors for measuring physiological and cognitive human states underpinning decision-making abilities. Physics-based human and robot motion models are developed along with new sensor simulations in order to capture the couplings between underwater behaviors and perception based on measurements from optical and sonar sensors. Real-time data acquisition pipelines are created to access and share data from both real and virtual sensors and robots, such that new methods for online planning and collaboration may be tested via human-in-the-loop demonstrations.

UnRealTHASC – a Cyber-Physical XR Testbed for Underwater Real-Time Human Autonomous Systems Collaboration

Quadruped robots are showing impressive abilities to navigate the real world. If they are to become more integrated into society, social trust in interactions with humans will become increasingly important. Additionally, robots will need to be adaptable to different humans based on individual preferences. In this work, we study the social interaction task of learning optimal handshakes for quadruped robots based on user preferences. While maintaining balance on three legs, we parameterize handshakes with a Central Pattern Generator consisting of an amplitude, frequency, stiffness, and duration. Through 10 binary choices between handshakes, we learn a belief model to fit individual preferences for 25 different subjects. Our results show that this is an effective strategy, with 76% of users feeling happy with their identified optimal handshake parameters, and 20% feeling neutral. Moreover, compared with random and test handshakes, the optimized handshakes have significantly decreased errors in amplitude and frequency, lower Dynamic Time Warping scores, and improved energy efficiency, all of which indicate robot synchronization to the user's preferences

Premium content

Downloads

Next from IEEE RO-MAN 2024

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

Similar lecture

Top-Down Effects on Anthropomorphism of a Robot