When several individuals collaborate on a shared task, their brain activities often synchronize. This phenomenon, known as Inter-brain Synchronization (IBS), is notable for inducing prosocial outcomes such as enhanced interpersonal feelings, including closeness, trust, empathy, and more. Further strengthening the IBS with the aid of external feedback would be beneficial for scenarios where those prosocial feelings play a vital role in interpersonal communication, such as rehabilitation between a therapist and a patient, motor skill learning between a teacher and a student, and group performance art. This paper investigates whether visual, auditory, and haptic feedback of the IBS level can further enhance its intensity, offering design recommendations for feedback systems in IBS. We report findings when three different types of feedback were provided: IBS level feedback by means of on-body projection mapping, sonification using chords, and vibration bands attached to the wrist.

Recent advances in multimodal Human-Robot Interaction (HRI) datasets emphasize the integration of speech and gestures, allowing robots to absorb explicit knowledge and tacit understanding. However, existing datasets primarily focus on elementary tasks like object pointing and pushing, limiting their applicability to complex domains. They prioritize simpler human command data but place less emphasis on training robots to correctly interpret tasks and respond appropriately. To address these gaps, we present the NatSGLD dataset, which was collected using a Wizard of Oz (WoZ) method, where participants interacted with a robot they believed to be autonomous. NatSGLD records humans' multimodal commands (speech and gestures), each paired with a demonstration trajectory and a Linear Temporal Logic (LTL) formula that provides a ground-truth interpretation of the commanded tasks. This dataset serves as a foundational resource for research at the intersection of HRI and machine learning. By providing multimodal inputs and detailed annotations, NatSGLD enables exploration in areas such as multimodal instruction following, plan recognition, and human-advisable reinforcement learning from demonstrations. We release the dataset and code under the MIT License at https://www.snehesh.com/natsgld/to support future HRI research.

Crowdsourcing data for Human-Robot Interaction (HRI) research remains a challenge, requiring scalable, flexible, and immersive methods to collect meaningful interaction data. This paper introduces CrowdHRI, a novel approach to gamify HRI data collection through a multiplayer mixed reality (MR) game. The proposed system integrates a web server and Unity-based client architecture, enabling users to schedule or join sessions dynamically. Through immersive MR, CrowdHRI offers realistic environments and supports customizable experimental setups, gathering high-fidelity data on human-robot interactions. The system includes automated metrics to capture interaction quality, alongside a robust data science framework for analysis. By addressing the limitations of existing platforms—such as restricted scalability and interaction fidelity CrowdHRI enables a wide range of experimental conditions and advances the field of HRI research. 1

There are a multitude of robotic application domains that touch on the field of human-robot interaction (HRI). From modern manufacturing involving human-robot teams, to personal care robots assisting the elderly, the roles that robots are being tasked with and the nature of interactions with humans are constantly shifting. Even the nature of interaction has changed to incorporate wearable technologies such as exoskeletons to enhance human capabilities, and advanced prosthetics to restore those abilities that have been lost. With this ever-evolving spectrum of HRI, the capacity of measurement science to evaluate, assess, and assure performance and safety struggles to keep up. Building on our previous five-workshop series on Test Methods and Metrics for Effective HRI, NIST presents a new series on evaluative methodologies for accelerating the pipeline from cutting-edge HRI research to state-of-practice. This workshop will address issues regarding 1) data collection and reporting for replicability and system validation, 2) test design and execution for performance verification, and 3) cross-modality artifact design for real-world application-adjacent technology transfer. The goal of this workshop is to accelerate and accommodate accessibility to HRI research results, and address the specific key performance indicators that would establish end-user trust and acceptance of emerging HRI technologies.

In an interdisciplinary and evolving research field like human-robot interaction, clear and precise results reporting is essential for study comparability and replicability. To address the lack of a standard for such reporting and, at the same time, provide guidance for novices in the field, we have developed a web-based reporting form to capture human-robot interaction studies, serving as a model for how conferences could adopt it into the submission pipeline. In this work, we present a formative evaluation of this form regarding its level of detail, format and clarity, and the perceived benefits for authors, reviewers, and the community as a whole. We report the expert review of nine researchers who highlight the substantial value of this tool. In addition, these experts also provide suggestions for improvements to its form and the addition of details surrounding qualitative reporting.

Musicians delicately control their bodies to generate music. Sometimes, their motions are too subtle to be captured by the human eye. To analyze how they move to produce the music, we need to estimate precise 4D human pose (3D pose over time). However, current state-of-the-art (SoTA) visual pose estimation algorithms struggle to produce accurate monocular 4D poses because of occlusions, partial views, and human-object interactions. They are limited by the viewing angle, pixel density, and sampling rate of the cameras and fail to estimate fast and subtle movements, such as in the musical effect of vibrato. We leverage the direct causal relationship between the music produced and the human motions creating them to address these challenges. We propose VioPose: a novel multimodal network that hierarchically estimates dynamics. High-level features are cascaded to low-level features and integrated into Bayesian updates. Our architecture is shown to produce accurate pose sequences, facilitating precise motion analysis, and outperforms SoTA. As part of this work, we collected the largest and the most diverse calibrated violin-playing dataset, including video, sound, and 3D motion capture poses. Code and dataset can be found in our project page https://sj-yoo.info/viopose/.