Stand-up motions are an indispensable part of humanoid robot soccer. A robot incapable of standing up by itself is removed from the game for some time. In this paper, we present our stand-up motions for the NAO robot. Our approach dates back to 2019 and has been evaluated and slightly expanded over the past six years. We claim that the main reason for failed stand-up attempts are large errors in the executed joint positions. By addressing such problems by either executing special motions to free up stuck limbs such as the arms, or by compensating large errors with other joints, we significantly increased the overall success rate of our stand-up routine. The motions presented in this paper are also used by several other teams in the Standard Platform League, which thereby achieve similar success rates, as shown in an analysis of videos from multiple tournaments.

-- Human motion retargeting for humanoid robots, transferring human motion data to robots for imitation, presents significant challenges but offers considerable potential for real-world applications. Traditionally, this process relies on human demonstrations captured through pose estimation or motion capture systems. In this paper, we explore a text-driven approach to mapping human motion to humanoids. T o address the inherent discrepancies between the generated motion representations and the kinematic constraints of humanoid robots, we propose an angle signal network based on norm-position and rotation loss (NPR Loss). It generates joint angles, which serve as inputs to a reinforcement learning-based whole-body joint motion control policy. The policy ensures tracking of the generated motions while maintaining the robot's stability during execution. Our experimental results demonstrate the efficacy of this approach, successfully transferring text-driven human motion to a real humanoid robot NAO. Humanoid robots have long been recognized for their potential to mimic human actions due to their anthropomorphic structure.

--This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot's responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot's imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. The field of robotics has come a long way in recent years, with significant advancements in the development of humanoid robots.

While traditional practices involve hiring human caregivers to serve meals and attend to basic needs, older adults often require continuous companionship and health monitoring. However, hiring human caregivers for this job costs a lot of money. However, using a robot like Nao could be cheaper and still helpful. This study explores the integration of humanoid robots, particularly Nao, in health monitoring and caregiving for older adults. Using a mixed-methods approach with a within-subject factorial design, we investigated the effectiveness of nonverbal communication modalities, including touch, gestures, and LED patterns, in enhancing human-robot interactions. Our results indicate that Nao's touch-based health monitoring was well-received by participants, with positive ratings across various dimensions. LED patterns were perceived as more effective and accurate compared to hand and head gestures. Moreover, longer interactions were associated with higher trust levels and perceived empathy, highlighting the importance of prolonged engagement in fostering trust in human-robot interactions. Despite limitations, our study contributes valuable insights into the potential of humanoid robots to improve health monitoring and caregiving for older adults.

As Robots become ever more important in our daily lives there's growing need for understanding how they're perceived by people. This study aims to investigate how the user perception of robots is influenced by displays of personality. Using LLMs and speech to text technology, we designed a within-subject study to compare two conditions: a personality-driven robot and a purely task-oriented, personality-neutral robot. Twelve participants, recruited from Socially Intelligent Robotics course at Vrije Universiteit Amsterdam, interacted with a robot Nao tasked with asking them a set of medical questions under both conditions. After completing both interactions, the participants completed a user experience questionnaire measuring their emotional states and robot perception using standardized questionnaires from the SRI and Psychology literature.

People deeply care about how fairly they are treated by robots. The established paradigm for probing fairness in Human-Robot Interaction (HRI) involves measuring the perception of the fairness of a robot at the conclusion of an interaction. However, such an approach is limited as interactions vary over time, potentially causing changes in fairness perceptions as well. To validate this idea, we conducted a 2x2 user study with a mixed design (N=40) where we investigated two factors: the timing of unfair robot actions (early or late in an interaction) and the beneficiary of those actions (either another robot or the participant). Our results show that fairness judgments are not static. They can shift based on the timing of unfair robot actions. Further, we explored using perceptions of three key factors (reduced welfare, conduct, and moral transgression) proposed by a Fairness Theory from Organizational Justice to predict momentary perceptions of fairness in our study. Interestingly, we found that the reduced welfare and moral transgression factors were better predictors than all factors together. Our findings reinforce the idea that unfair robot behavior can shape perceptions of group dynamics and trust towards a robot and pave the path to future research directions on moment-to-moment fairness perceptions

Head movements are crucial for social human-human interaction. They can transmit important cues (e.g., joint attention, speaker detection) that cannot be achieved with verbal interaction alone. This advantage also holds for human-robot interaction. Even though modeling human motions through generative AI models has become an active research area within robotics in recent years, the use of these methods for producing head movements in human-robot interaction remains underexplored. In this work, we employed a generative AI pipeline to produce human-like head movements for a Nao humanoid robot. In addition, we tested the system on a real-time active-speaker tracking task in a group conversation setting. Overall, the results show that the Nao robot successfully imitates human head movements in a natural manner while actively tracking the speakers during the conversation. Code and data from this study are available at https://github.com/dingdingding60/Humanoids2024HRI

To be able to play soccer, a robot needs a good estimate of its current position on the field. Ideally, multiple features are visible that have known locations. By applying trigonometry we can estimate the viewpoint from where this observation was actually made. Given that the Nao robots of the Standard Platform League have quite a limited field of view, a given camera frame typically only allows for one or two points to be recognized. In this paper we propose a method for determining the (x, y) coordinates on the field and the height h of the camera from the geometry of a simplified tetrahedron. This configuration is formed by two observed points on the ground plane plus the gravitational direction. When the distance between the two points is known, and the directions to the points plus the gravitational direction are measured, all dimensions of the tetrahedron can be determined. By performing these calculations with rational trigonometry instead of classical trigonometry, the computations turn out to be 28.7% faster, with equal numerical accuracy. The position of the head of the Nao can also be externally measured with the OptiTrack system. The difference between externally measured and internally predicted position from sensor data gives us mean absolute errors in the 3-6 centimeters range, when we estimated the gravitational direction from the vanishing point of the outer edges of the goal posts.

As robots grow more and more integrated into numerous industries, it is critical to comprehend how humans respond to their failures. This paper systematically studies how trust dynamics and system design are affected by human responses to robot failures. The three-stage survey used in the study provides a thorough understanding of human-robot interactions. While the second stage concentrates on interaction details, such as robot precision and error acknowledgment, the first stage collects demographic data and initial levels of trust. In the last phase, participants' perceptions are examined after the encounter, and trust dynamics, forgiveness, and propensity to suggest robotic technologies are evaluated. Results show that participants' trust in robotic technologies increased significantly when robots acknowledged their errors or limitations to participants and their willingness to suggest robots for activities in the future points to a favorable change in perception, emphasizing the role that direct engagement has in influencing trust dynamics. By providing useful advice for creating more sympathetic, responsive, and reliable robotic systems, the study advances the science of human-robot interaction and promotes a wider adoption of robotic technologies.

This paper presents a new software framework for HRI experimentation with the sixth version of the common NAO robot produced by the United Robotics Group. Embracing the common demand of researchers for better performance and new features for NAO, the authors took advantage of the ability to run ROS2 onboard on the NAO to develop a framework independent of the APIs provided by the manufacturer. Such a system provides NAO with not only the basic skills of a humanoid robot such as walking and reproducing movements of interest but also features often used in HRI such as: speech recognition/synthesis, face and object detention, and the use of Generative Pre-trained Transformer (GPT) models for conversation. The developed code is therefore configured as a ready-to-use but also highly expandable and improvable tool thanks to the possibilities provided by the ROS community.