CORTEXA
← Browse
arxivcs.ROcs.HC2026-07-13

Analysis of Mutual and Referential Human and Robot Gazes in a Collaborative Word Association Game

Jens V. Rüppel, Tim Schreiter, Andrey Rudenko, Achim J. Lilienthal

Robot gaze is a major component of human-robot dialogue coordination. Most studies of gaze in human-robot dialogue focus on face-to-face social conversations, but little is known about gaze in demanding task-focused interactions. In this paper, we investigate how the gaze of a robot game partner affects human visual attention and if humans tend to direct confirmation-seeking gazes towards the robot. In our study, we let participants play a collaborative word association game with a NAO robot acting as an embodied, LLM-driven conversational partner. Our experiments are conducted under two conditions, which implement mutual and referential gazes of the robot respectively. We record participants' gaze using eye tracking glasses and analyze the interactions using gaze coordinates, speech segments, key events and areas of interests. We find that robot gaze orientation does not affect the time to first fixation on words the robot proposed. We also find that participants gaze more often at the robot when their dialogue line contains confirmation requests, compared to when it does not. Our results indicate (likely also due to the cognitively demanding nature of the game) that the verbal aspect of this task overshadows the effects of referential robot gaze. These findings offer valuable insights for designing and validating robot gaze and turn-taking behavior in collaborative tasks which require coordination and efficient communication.

View free PDFSource page

Related papers

arxivcs.ROcs.HCcs.LG2026-07-22

Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning

Nicolas Kosanovic, Jordan Dowdy, Jean Chagas Vaz

Full-sized humanoid robot capabilities have grown exponentially in recent years, aiming towards general-purpose deployment in human environments. A popular control method used by manufacturers utilizes Virtual Reality for upper-body teleoperation and Reinforcement Learning for lo…

View free PDFSource page
arxivcs.ROcs.HCeess.SP2026-07-21

How defensive driving enhances driving safety: A driving simulator study on drivers' defensive driving behaviors

Xinzheng Wu, Junyi Chen, Shaolingfeng Ye, Yong Shen

Defensive driving is widely recognized as an advanced driving skill. However, whether and how defensive driving affects driving safety remains insufficiently investigated. This study examines the behavioral characteristics of defensive driving, its impact on driving safety, and t…

View free PDFSource page
arxivcs.ROcs.HC2026-07-31

STAGE: STyle-controllable Action GEneration for personalized autonomous driving

Zihao Liu, Xing Liu, Yizhai Zhang, Panfeng Huang

Driving style refers to the behavioral preferences that drivers maintain during driving, shaped by their diverse experiences, habits, and needs, and is typically reflected in varying levels of aggressiveness. If humans choose to use autonomous driving systems, they would expect t…

View free PDFSource page
arxivcs.ROcs.CVcs.HC2026-07-20

From Sign Language Generation to Humanoid Execution: Vision-Language Guided Retargeting with Collision Mitigation

Nabeela Khan, Bowen Wu, Runwu Shi, Benjamin Yen, Takeshi Ashizawa, Carlos Toshinori Ishi, et al.

Recent sign language generation (SLG) systems increasingly output dense 3D body representations, which better preserve full-body kinematics and geometry for downstream embodiment on humanoid robots. However, these generated motions frequently exhibit self-intersections such as ha…

View free PDFSource page
arxivcs.RO2026-07-20

Receiver-Centered Robot-to-Human Handover with Grasp-Aware Object Orientation

Federico Biagi, Dario Onfiani, Simone Silenzi, Luigi Biagiotti

Collaborative robots are increasingly sharing workspaces with human operators, making tool handover a frequent and safety-critical micro-interaction. However, traditional static handovers often lead to awkward grasps when handling asymmetric industrial tools. This paper presents…

View free PDFSource page
arxivcs.RO2026-07-31

TacPrint: A Wearable Fingertip Tactile Sensor for Human-to-Robot Contact Reproduction

Yongxi Liu, Chaofan Zhang, Xingyu Zhang, Xiangyin Bao, Boyue Zhang, Shaowei Cui, et al.

Human-centric data collection is emerging as a significant paradigm for robot skill acquisition, but seamlessly integrating low-cost, scalable tactile sensing systems that capture fine-grained fingertip interactions without compromising natural operation remains a key challenge.…

View free PDFSource page