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Researcher profile

Xinyan Yu

Xinyan Yu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Human-Computer InteractionMachine Learning

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Published work

3 published item(s)

preprint2026arXiv

Feel the Presence: The Effects of Haptic Sensation on VR-Based Human-Robot Interaction

Virtual reality (VR) has been increasingly utilised as a simulation tool for human-robot interaction (HRI) studies due to its ability to facilitate fast and flexible prototyping. Despite efforts to achieve high validity in VR studies, haptic sensation, an essential sensory modality for perception and a critical factor in enhancing VR realism, is often absent from these experiments. Studying an interactive robot help-seeking scenario, we used a VR simulation with haptic gloves that provide highly realistic tactile and force feedback to examine the effects of haptic sensation on VR-based HRI. We compared participants' sense of presence and their assessments of the robot to a traditional setup using hand controllers. Our results indicate that haptic sensation enhanced participants' social and self-presence in VR and fostered more diverse and natural bodily engagement. Additionally, haptic sensations significantly influenced participants' affective-related perceptions of the robot. Our study provides insights to guide HRI researchers in building VR-based simulations that better align with their study contexts and objectives.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

The UnScripted Trip: Fostering Policy Discussion on Future Human-Vehicle Collaboration in Autonomous Driving Through Design-Oriented Methods

The rapid advancement of autonomous vehicle (AV) technologies is fundamentally reshaping paradigms of human-vehicle collaboration, raising not only an urgent need for innovative design solutions but also for policies that address corresponding broader tensions in society. To bridge the gap between HCI research and policy making, this workshop will bring together researchers and practitioners in the automotive community to explore AV policy directions through collaborative speculation on the future of AVs. We designed The UnScripted Trip, a card game rooted in fictional narratives of autonomous mobility, to surface tensions around human-vehicle collaboration in future AV scenarios and to provoke critical reflections on design solutions and policy directions. Our goal is to provide an engaging, participatory space and method for automotive researchers, designers, and industry practitioners to collectively explore and shape the future of human-vehicle collaboration and its policy implications.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Towards Steering without Sacrifice: Principled Training of Steering Vectors for Prompt-only Interventions

Recently, steering vectors (SVs) have emerged as an effective and lightweight approach to steer behaviors of large language models (LLMs), among which fine-tuned SVs are more effective than optimization-free ones. However, current approaches to fine-tuned SVs suffer from two limitations. First, they require careful selection of steering factors on a per-SV basis to balance steering effectiveness and generation quality at inference time. Second, they operate as full-sequence SVs (FSSVs), which can sacrifice generation quality regardless of factor selection due to excessive intervention on the model generation process. To address the first limitation, we propose joint training of steering factors and directions, such that post-hoc factor selection is no longer required. Using neural network scaling theory, we find that moderately large initialization sizes and learning rates for steering factors are essential for stability and efficiency of joint training. To tackle the second limitation, we draw inspiration from representation fine-tuning and introduce Prompt-only SV (PrOSV), an SV that intervenes only on a few prompt tokens. Our empirical results show that PrOSV outperforms traditional FSSVs on AxBench when using our joint training scheme. We also find that PrOSV achieves a better tradeoff between general model utility and adversarial robustness than FSSV.

0 citations0 reviewsNo code linkedOpen access