![ ](./assets/banner2.png width="480px" align=left)   [Home](index.html)  [Speakers](speakers.html)  [Program](program.html)  [Call for Papers](call.html)  [Accepted Papers](papers.html)  [Organizers](organizers.html) **Accepted papers** + Learning Early Social Maneuvers for Enhanced Social Navigation, Y. Yildirim, M. Suzer, and E. Ugur. [PDF](https://drive.google.com/file/d/13qFs0l4OJgRAMhjUSzwPz7z6P60umTZD/view?usp=sharing) **Abstract:** Socially compliant navigation is an integral part of safety features in Human-Robot Interaction. Traditional approaches to mobile navigation prioritize physical aspects, such as efficiency. Still, the social competence of robots has gained traction as the attributed trust towards them is a crucial factor influencing their acceptance in our daily lives. Recent techniques to improve the social compliance of navigation often rely on predefined features or reward functions, introducing assumptions about social human behavior. To address this limitation, we propose a novel Learning from Demonstration (LfD) framework for social navigation that exclusively utilizes raw sensory data to address this limitation. Additionally, the proposed system contains mechanisms to consider the future paths of the surrounding pedestrians, acknowledging the temporal aspect of the problem. The final product is expected to reduce the anxiety of people sharing their environment with a mobile robot, helping them trust that the robot is aware of their presence and will not harm them. As the framework is currently being developed, we outline its components, present experimental results, and discuss future work towards realizing this framework. --- + Is that robot trustworthy to assist me in a reeducation session with NDD children?, S. M. Anzalone, M. Ahmida, J. Zou, D. Cohen, and E. Zibetti. [PDF](https://drive.google.com/file/d/1FWwBiiFt-sNEa-r9fYWNkSt1FjBBObn6/view?usp=sharing) **Abstract:** The context of Human Robot collaborative scenarios and the rapid growth of Socially Assistive Robotics (SAR) in sensible scenarios requires a deeper analysis of the trust that professionals have on such machines. Focusing on the use of robots in reeducation therapies of children with neurodevelopmental disorders (NDD), this paper explores the use of the Multi-Dimensional Measure of Trust highlighting strengths and limits of this scale in this particular context. --- + Bidirectional Human Interactive AI Framework for Social Robot Navigation, T. Girgin, E. Girgin, Y. Yildirim, E. Ugur, and M. Haklidir. [PDF ](https://drive.google.com/file/d/1m-wRkV9rL4YGU9FrV8neMvq9ST8DsElE/view?usp=sharing) **Abstract:** Trustworthiness is a crucial concept in the con- text of human-robot interaction. Cooperative robots must be transparent regarding their decision-making process, especially when operating in a human-oriented environment. This paper presents a comprehensive end-to-end framework aimed at fostering trustworthy bidirectional human-robot interaction in collaborative environments for the social navigation of mobile robots. Our method enables a mobile robot to predict the trajectory of people and adjust its route in a socially-aware manner. In case of conflict between human and robot decisions, detected through visual examination, the route is dynamically modified based on human preference while verbal communica- tion is maintained. We present our pipeline, framework design, and preliminary experiments that form the foundation of our proposition. --- + Users’ Perception on Appropriateness of Robotic Coaching Assistant’s Disclosure Behaviors, A. F. Nilgar, M. Dietrich, and K. van Laerhoven. [PDF](https://drive.google.com/file/d/1qJ9DV9I--6IJ2hGV71s4cZ71hH-xuzgg/view?usp=sharing) **Abstract:** Social robots have emerged as valuable contributors to individuals’ well-being coaching. Notably, their integration into long-term human coaching trials shows particular promise, emphasizing a complementary role alongside human coaches rather than outright replacement. In this context, robots serve as supportive entities during coaching sessions, offering insights based on their knowledge about the users’ well-being and activity. Traditionally, such insights have been gathered through methods like written self-reports or wearable data visualizations. However, the disclosure of people’s information by a robot raises concerns regarding privacy, appropriateness, and trust. To address this, we conducted an initial study [n = 22] to quantify participants’ perceptions of privacy regarding disclosures made by robot coaching assistant. The study was conducted online, presenting participants with six prerecorded scenarios illustrating various types of information disclosure and the robot’s role, ranging from active on-demand to proactive communication conditions. --- + Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving, Z. Mao, D-Y. Jhong, A. Wang, and I. Ruchkin. [PDF](https://drive.google.com/file/d/1KzLMbd815R3nFUbOUiTrN0RwXIgOQCbg/view?usp=sharing) **Abstract:** Out-of-distribution (OOD) detection is essential in autonomous driving, to determine when learning-based components encounter unexpected inputs. Traditional detectors typically use encoder models with fixed settings, thus lacking effective human interaction capabilities. With the rise of large foundation models, multimodal inputs offer the possibility of taking human language as a latent representation, thus enabling language-defined OOD detection. In this paper, we use the cosine similarity of image and text representations encoded by the multimodal model CLIP as a new representation to improve the transparency and controllability of latent encodings used for visual anomaly detection. We compare our approach with existing pre-trained encoders that can only produce latent representations that are meaningless from the user’s standpoint. Our experiments on realistic driving data show that the language-based latent representation performs better than the traditional representation of the vision encoder and helps improve the detection performance when combined with standard representations. --- + Enhancing Robot Explanation Capabilities through Vision-Language Models: a Preliminary Study by Interpreting Visual Inputs for Improved Human-Robot Interaction, D. Sobri-Hildago, M. A. Gonzalez-Santamarta, A. M. Guerrero-Higueras, F. J. Rodriguez-Lera, and V. Matellan-Olivera **Abstract:** This paper presents an improved system based on our prior work, designed to create explanations for autonomous robot actions during Human-Robot Interaction (HRI). Pre- viously, we developed a system that used Large Language Models (LLMs) to interpret logs and produce natural language explanations. In this study, we expand our approach by incor- porating Vision-Language Models (VLMs), enabling the system to analyze textual logs with the added context of visual input. This method allows for generating explanations that combine data from the robot’s logs and the images it captures. We tested this enhanced system on a basic navigation task where the robot needs to avoid a human obstacle. The findings from this preliminary study indicate that adding visual interpretation improves our system’s explanations by precisely identifying obstacles and increasing the accuracy of the explanations provided.