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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Sunday, Kissinger Anvari, Soraya S. Li, Yiwei Sun, Junwei Wehbe, Rina R. Neyedli, Heather Batmaz, Anil Ufuk Machuca, Mayra Donaji Barrera Faculty of Computer Science VR Lab. Dalhousie University Halifax NS Canada HCI4Good Dalhousie University Halifax NS Canada University of Western Ontario Toronto ON Canada School of Interactive Arts and Technology Simon Fraser University Vancouver BC Canada HCI4Good Faculty of Computer Science Dalhousie University Halifax NS Canada School Health & Human Performance Dalhousie University Halifax NS Canada Department of Computer Science & Software Engineering Concordia University Montreal QC Canada Faculty of Computer Science Dalhousie University Halifax NS Canada

ISBN: (纸本)9798400713958

Advances in Virtual Reality (VR) technology have redefined sports training, offering a new modality for athletes to prepare for competitions. This paper explores the coaching strategies used to train novice table tennis players in VR. Through interviews with the coach and participant feedback, we identified key strategies such as personalized feedback, table division, and a structured training regimen for skill development in VR. Challenges include limited facial awareness and the absence of virtual cheering, which impacted the coach’s ability to engage fully with participants. These findings offer valuable insights into coaching strategies in VR environments, contributing to developing more effective and engaging VR training systems. © 2025 Copyright held by the owner/author(s).

关键词： Feedback Sports Table Tennis Training Virtual Reality (VR)

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach

arXiv

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arXiv 2025年

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Intelligent Robotics Laboratory Oakland University Michigan United States

—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. © 2025, CC BY-NC-ND.

关键词： Anthropomorphic robots

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Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Agents to Enhance Young Adults' Career Exploration 25

Letters from Future Self: Augmenting the Letter-Exchange Exe...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Jeon, Hayeon Yoon, Suhwoo Lee, Keyeun Kim, Seo Hyeong Kim, Esther Hehsun Cho, Seonghye Ko, Yena Yang, Soeun Dabbish, Laura Zimmerman, John Kim, Eun-Mee Lim, Hajin Hci+d Lab. Seoul National University Seoul South Korea Department of Communication Seoul National University Seoul South Korea Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA United States HCI Institute Carnegie Mellon University Pittsburgh PA United States

ISBN: (纸本)9798400713941

Young adults often encounter challenges in career exploration. Self-guided interventions, such as the letter-exchange exercise, where participants envision and adopt the perspective of their future selves by exchanging letters with their envisioned future selves, can support career development. However, the broader adoption of such interventions may be limited without structured guidance. To address this, we integrated Large Language Model (LLM)-based agents that simulate participants' future selves into the letter-exchange exercise and evaluated their effectiveness. A one-week experiment (N=36) compared three conditions: (1) participants manually writing replies to themselves from the perspective of their future selves (baseline), (2) future-self agents generating letters to participants, and (3) future-self agents engaging in chat conversations with participants. Results indicated that exchanging letters with future-self agents enhanced participants' engagement during the exercise, while overall benefits of the intervention on future orientation, career self-concept, and psychological support remained comparable across conditions. We discuss design implications for AI-augmented interventions for supporting young adults' career exploration. © 2025 Copyright held by the owner/author(s).

关键词： AI clone career exploration career pursuits future self future-self agents letter-exchange exercise letter-writing exercise LLM LLM-based agent self-guided interventions writing interventions Young adults

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RE:BT-Espresso: Improving Interpretability and Expressivity of Behavior Trees Learned from Robot Demonstrations

RE:BT-Espresso: Improving Interpretability and Expressivity ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Adam Wathieu Thomas R. Groechel Haemin Jenny Lee Chloe Kuo Maja J. Matarić Department of Computer Science Northwestern University Evanston IL USA Department of Computer Science Interaction Lab University of Southern California Los Angeles CA USA

ISBN: (数字)9781728196817

ISBN: (纸本)9781728196824

Behavior trees (BTs) are hierarchical agent control architectures popular for robot task-level planning that can be autonomously learned from robot demonstrations via decision tree (DT) intermediaries, making them accessible to non-expert users. Conversion algorithms from DTs to BTs, such as the BT-Espresso algorithm, focus on replicating DT logic in a BT format but do not exploit the strengths of the BT architecture. We introduce the Representation Exploitation of BT-Espresso (RE:BT-Espresso) algorithm, which builds on BT-Espresso and improves the learned BT's interpretability and expressivity. RE:BT-Espresso improves interpretability by removing logical redundancies in the generated BTs and improves expressivity by exploiting desired BT structures, such as adding Inverter nodes, Repeater sequences, and Parallel Selector Action nodes that gives the user a choice of actions for state spaces that did not resolve to a concise action in the DT. The RE:BT-Espresso algorithm was evaluated against BT-Espresso using demonstration data synthesized by BTs. When compared to the synthesized BTs using graph edit distance (GED), RE:BT-Espresso outscored BT-Espresso on 54 subtrees, tied on 178, and lost on 2. Further, the proposed reduction strategies reduced the number of nodes in a generated tree by a median of 7.82%. The results validate improved interpretability and expressivity of learned RE:BT-Espresso task-level BT policies from robot demonstration.

关键词： Automation Redundancy Repeaters Inverters Behavioral sciences Planning Decision trees

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A Passive Variable Impedance Control Strategy with Viscoelastic Parameters Estimation of Soft Tissues for Safe Ultrasonography

A Passive Variable Impedance Control Strategy with Viscoelas...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Luca Beber Edoardo Lamon Davide Nardi Daniele Fontanelli Matteo Saveriano Luigi Palopoli Department of Industrial Engineering Università di Trento Trento Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the context of telehealth, robotic approaches have proven a valuable solution to in-person visits in remote areas, with decreased costs for patients and infection risks. In particular, in ultrasonography, robots have the potential to reproduce the skills required to acquire high-quality images while reducing the sonographer’s physical efforts. In this paper, we address the control of the interaction of the probe with the patient’s body, a critical aspect of ensuring safe and effective ultrasonography. We introduce a novel approach based on variable impedance control, allowing the real-time optimisation of compliant controller parameters during ultrasound procedures. This optimisation is formulated as a quadratic programming problem and incorporates physical constraints derived from viscoelastic parameter estimations. Safety and passivity constraints, including an energy tank, are also integrated to minimise potential risks during human-robot interaction. The proposed method’s efficacy is demonstrated through experiments on a patient’s dummy torso, highlighting its potential for achieving safe behaviour and accurate force control during ultrasound procedures, even in cases of contact loss.

关键词： Torso Parameter estimation Tracking Telemedicine Ultrasonography Safety Impedance

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Mediating The Marginal: A Quantitative Analysis of Curated LGBTQ+ Content on Instagram 25

Mediating The Marginal: A Quantitative Analysis of Curated L...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Souza, Garrett Lutz, Nina Turner, Katlyn M Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Human Centered Design and Engineering University of Washington Seattle WA United States Media Lab Massachusetts Institute of Technology Cambridge MA United States

ISBN: (纸本)9798400713941

Control and curation of dominant visual culture - rendering who and what is visible - is central to identity formation, particularly for LGBTQ+ communities relying on digital spaces for safe self-expression. In this work, we analyze Instagram as a site of algorithmic visual curation, performing a quantitative analysis of algorithmically mediated image feeds delivered to a gay-coded user. Our persona account exclusively followed #gay and #instagay feeds, and engaged in content within these discursive spaces to seed algorithmic content promotion to a normative gay user. We present an analysis of skin tone presentations, emoji usage, and engagement metrics alongside analysis of generative outputs of dominant visual trends within the #gay search and Explore feeds. We observe content depicting darker-skinned individuals has higher engagement yet less algorithmic promotion relative to lighter skin tones, while hypermasculine and homonormative content is heavily promoted. These results suggest that, while marginalized positionalities have certainly been rendered more visible through social media platforms, this visibility is increasingly contingent on assimilation to normative ideals through algorithmically determined modes that are not necessarily consistent with user choices, preferences, or realities. © 2025 Copyright held by the owner/author(s).

关键词： LGBTQ+ Visual Content Recommendation Algorithms Social Networks

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Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model

Controllable Factuality in Document-Grounded Dialog Systems ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Daheim, Nico Thulke, David Dugast, Christian Ney, Hermann Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany Human Language Technology and Pattern Recognition RWTH Aachen University Germany AppTek GmbH

In this work, we present a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes' theorem. One component is a traditional ungrounded response generation model and the other component models the reconstruction of the grounding document based on the dialog context and generated response. We propose different approximate decoding schemes and evaluate our approach on multiple open-domain and task-oriented document-grounded dialog datasets. Our experiments show that the model is more factual in terms of automatic factuality metrics than the baseline model. Furthermore, we outline how introducing scaling factors between the components allows for controlling the tradeoff between factuality and fluency in the model output. Finally, we compare our approach to a recently proposed method to control factuality in grounded dialog, CTRL (Rashkin et al., 2021), and show that both approaches can be combined to achieve additional improvements. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Building Intelligent and Intelligible AI: A Framework for human-like Autonomy and Explainability in Critical Infrastructure

Building Intelligent and Intelligible AI: A Framework for Hu...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Srikanth Vemula Kainen DuFresne-To Faculty of Computer Science Department Human-Centric eXplainable Intelligence Lab (hcXiL) College of Saint Benidict & Saint John’s University Collegeville MN Under Graduate Student in Computer Science Department Human-Centric eXplainable Intelligence Lab (hcXiL) College of Saint Benidict & Saint John’s University Collegeville MN

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

This paper presents a novel AI system architecture designed for critical infrastructure, emphasizing the importance of human-robot interaction (HRI) in improving the explainability of AI decision-making. The proposed approach combines Case-Based Reasoning (CBR) and ontology to create a dynamic AI framework that ensures transparency and trustworthiness in autonomous operations. In which the Case-Based Reasoning (CBR) component employs prototype and exemplar methods from cognitive psychology to mimic human decision-making, while the ontology component organizes the knowledge base, enhancing clarity and comprehension. The proposed system architecture consists of three interconnected components that work together to improve the AI’s explainability and adaptability in dynamic environments. These components include: 1) a Case-Based Reasoning (CBR)-powered decision-making module that enables the AI to learn from past experiences and justify its actions in a human-understandable format; 2) an ontology-guided knowledge framework that provides a structured and semantic representation of information to guide AI operations; and 3) an HRI mechanism that facilitates effective collaboration between humans and the AI system, ensuring that autonomous decisions are transparent and aligned with human oversight. We evaluate the proposed architecture in a simulated environment, demonstrating improved AI explainability and reliability. Our findings show that the integration of CBR and ontology links decision-making with transparency, highlighting the role of HRI in accountable AI for critical infrastructure. The paper discusses the design and outcomes of the study, paving the way for the development of more transparent and trustworthy AI systems for critical infrastructure.

关键词： Technological innovation Decision making human-robot interaction Systems architecture Ontologies Cognition Critical infrastructure Reliability Artificial intelligence Testing

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SemanticAdapt: Optimization-based Adaptation of Mixed Reality Layouts Leveraging Virtual-Physical Semantic Connections 21

SemanticAdapt: Optimization-based Adaptation of Mixed Realit...

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34th Annual ACM Symposium on User Interface Software and Technology, UIST 2021

作者： Cheng, Yifei Yan, Yukang Yi, Xin Shi, Yuanchun Lindlbauer, David Department of Computer Science and Technology Tsinghua University China Swarthmore College United States Institute for Network Sciences and Cyberspace Tsinghua University China Human-Computer Interaction Institute Carnegie Mellon University United States

ISBN: (纸本)9781450386357

We present an optimization-based approach that automatically adapts Mixed Reality (MR) interfaces to different physical environments. Current MR layouts, including the position and scale of virtual interface elements, need to be manually adapted by users whenever they move between environments, and whenever they switch tasks. This process is tedious and time consuming, and arguably needs to be automated for MR systems to be beneficial for end users. We contribute an approach that formulates this challenge as a combinatorial optimization problem and automatically decides the placement of virtual interface elements in new environments. To achieve this, we exploit the semantic association between the virtual interface elements and physical objects in an environment. Our optimization furthermore considers the utility of elements for users' current task, layout factors, and spatio-temporal consistency to previous layouts. All those factors are combined in a single linear program, which is used to adapt the layout of MR interfaces in real time. We demonstrate a set of application scenarios, showcasing the versatility and applicability of our approach. Finally, we show that compared to a naive adaptive baseline approach that does not take semantic associations into account, our approach decreased the number of manual interface adaptations by 33%. © 2021 ACM.

关键词： Mixed reality

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Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm

arXiv

引用

arXiv 2024年

作者： Beber, Luca Lamon, Edoardo Moretti, Giacomo Fontanelli, Daniele Saveriano, Matteo Palopoli, Luigi Department of Information Engineering and Computer Science Università di Trento Trento Italy DRIM Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy Department of Industrial Engineering Università di Trento Trento Italy

This paper introduces a new method for estimating the penetration of the end effector and the parameters of a soft body using a collaborative robotic arm. This is possible using the dimensionality reduction method that simplifies the Hunt-Crossley model. The parameters can be found without a force sensor thanks to the information of the robotic arm controller. To achieve an online estimation, an extended Kalman filter is employed, which embeds the contact dynamic model. The algorithm is tested with various types of silicone, including samples with hard intrusions to simulate cancerous cells within a soft tissue. The results indicate that this technique can accurately determine the parameters and estimate the penetration of the end effector into the soft body. These promising preliminary results demonstrate the potential for robots to serve as an effective tool for early-stage cancer diagnostics. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic arms

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