XR$^3$: An Extended Reality Platform for Social-Physical Human-Robot Interaction

• URL: http://arxiv.org/abs/2601.12395v3
• Date: Fri, 23 Jan 2026 11:54:41 GMT
• Title: XR$^3$: An Extended Reality Platform for Social-Physical Human-Robot Interaction
• Abstract summary: We present XR$3$, a co-located operator and a hidden dual-VR-headset platform for HRI research.<n>The robot's upper-body motion, head and gaze behavior, and facial expressions are mapped from the operator's tracked limbs and face signals.<n>We detail the system design and calibration, and demonstrate the platform in a touch-based Wizard-of-Oz study.
• Score: 7.5509967369489965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social-physical human-robot interaction (spHRI) is difficult to study: building and programming robots that integrate multiple interaction modalities is costly and slow, while VR-based prototypes often lack physical contact, breaking users' visuo-tactile expectations. We present XR$^3$, a co-located dual-VR-headset platform for HRI research in which an attendee and a hidden operator share the same physical space while experiencing different virtual embodiments. The attendee sees an expressive virtual robot that interacts face-to-face in a shared virtual environment. In real time, the robot's upper-body motion, head and gaze behavior, and facial expressions are mapped from the operator's tracked limbs and face signals. Because the operator is co-present and calibrated in the same coordinate frame, the operator can also touch the attendee, enabling perceived robot touch synchronized with the robot's visible hands. Finger and hand motion is mapped to the robot avatar using inverse kinematics to support precise contact. Beyond motion retargeting, XR$^3$ supports social retargeting of multiple nonverbal cues that can be experimentally varied while keeping physical interaction constant. We detail the system design and calibration, and demonstrate the platform in a touch-based Wizard-of-Oz study, lowering the barrier to prototyping and evaluating embodied, contact-based robot behaviors.

Related papers

• Validating Virtual Reality for Studying Multimodal Human-Robot Interaction in Socially Aware Robot Navigation [5.566807756855081]
  We present a VR prototype and evaluate its suitability for studying multimodal human-robot interaction (HRI) in socially aware navigation.<n>Specifically, we investigate whether VR preserves the multimodal interaction dynamics observed in real-world human-robot co-navigation.<n>Our results show that participants perceive the robot's socially aware navigation similarly in VR and in the real world.
  arXiv  Detail & Related papers  (2026-07-10T10:23:57Z)
• Initiation of Interaction Detection Framework using a Nonverbal Cue for Human-Robot Interaction [15.228284553815593]
  This paper describes an initiation of interaction(IoI) detection framework based on audio and vision sensor fusion.<n>In the proposed framework, the robot has its own audio and vision sensors, and can employ external vision sensor for stable human detection and tracking.
  arXiv  Detail & Related papers  (2026-05-11T07:07:01Z)
• Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery [64.82869118243723]
  We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement.<n>Motivated by the promise of BCI-driven robotics, this system bridges neural signals with physical control by deploying offline-pretrained decoders in a zero-shot manner.
  arXiv  Detail & Related papers  (2026-03-03T17:41:42Z)
• RobotSeg: A Model and Dataset for Segmenting Robots in Image and Video [56.9581053843815]
  We introduce RobotSeg, a foundation model for robot segmentation in image and video.<n>It addresses the lack of adaptation to articulated robots, reliance on manual prompts, and the need for per-frame training mask annotations.<n>It achieves state-of-the-art performance on both images and videos.
  arXiv  Detail & Related papers  (2025-11-28T07:51:02Z)
• Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis [51.95817740348585]
  Human-X is a novel framework designed to enable immersive and physically plausible human interactions across diverse entities.<n>Our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner.<n>Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction.
  arXiv  Detail & Related papers  (2025-08-04T06:35:48Z)
• Recognizing Actions from Robotic View for Natural Human-Robot Interaction [52.00935005918032]
  Natural Human-Robot Interaction (N-HRI) requires robots to recognize human actions at varying distances and states, regardless of whether the robot itself is in motion or stationary.<n>Existing benchmarks for N-HRI fail to address the unique complexities in N-HRI due to limited data, modalities, task categories, and diversity of subjects and environments.<n>We introduce (Action from Robotic View) a large-scale dataset for perception-centric robotic views prevalent in mobile service robots.
  arXiv  Detail & Related papers  (2025-07-30T09:48:34Z)
• FABG : End-to-end Imitation Learning for Embodied Affective Human-Robot Interaction [3.8177867835232004]
  This paper proposes FABG (Facial Affective Behavior Generation), an end-to-end imitation learning system for human-robot interaction.<n>We develop an immersive virtual reality (VR) demonstration system that allows operators to perceive stereoscopic environments.<n>We deploy FABG on a real-world 25-degree-of-freedom humanoid robot, validating its effectiveness through four fundamental interaction tasks.
  arXiv  Detail & Related papers  (2025-03-03T09:58:04Z)
• Towards Embedding Dynamic Personas in Interactive Robots: Masquerading Animated Social Kinematics (MASK) [10.351714893090964]
  This paper presents the design and development of an innovative interactive robotic system to enhance audience engagement using character-like personas. Built upon the foundations of persona-driven dialog agents, this work extends the agent's application to the physical realm, employing robots to provide a more captivating and interactive experience.
  arXiv  Detail & Related papers  (2024-03-15T06:22:32Z)
• Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction [9.806227900768926]
  We propose to model social motion forecasting in a shared human-robot representation space. ECHO operates in the aforementioned shared space to predict the future motions of the agents encountered in social scenarios. We evaluate our model in multi-person and human-robot motion forecasting tasks and obtain state-of-the-art performance by a large margin.
  arXiv  Detail & Related papers  (2024-02-07T11:37:14Z)
• A ROS Architecture for Personalised HRI with a Bartender Social Robot [61.843727637976045]
  BRILLO project has the overall goal of creating an autonomous robotic bartender that can interact with customers while accomplishing its bartending tasks. We present the developed three-layers ROS architecture integrating a perception layer managing the processing of different social signals, a decision-making layer for handling multi-party interactions, and an execution layer controlling the behaviour of a complex robot composed of arms and a face.
  arXiv  Detail & Related papers  (2022-03-13T11:33:06Z)
• Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration [83.4081612443128]
  We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
  arXiv  Detail & Related papers  (2021-03-06T09:14:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.