CARIS: A Context-Adaptable Robot Interface System for Personalized and Scalable Human-Robot Interaction

2025-08-30

AI Summary

Existing Wizard-of-Oz (WoZ) robotic interaction systems are largely confined to single, isolated scenarios and lack adaptability across environments, users, and platforms. To address this limitation, we propose the Context-Adaptive Robotic Interaction System (CARIS)—the first modular WoZ control framework designed for cross-scenario reusability. CARIS integrates teleoperation, human perception, real-time conversational interaction, behavioral state synchronization, and multimodal data logging. Its configurable architecture enables rapid deployment and extensible functionality, significantly enhancing applicability and research flexibility in heterogeneous domains such as mental health companionship and venue navigation. Two pilot studies validate CARIS’s effectiveness and identify key improvement directions, including communication-motion co-optimization. The system is open-sourced to advance standardization and reproducibility in empirical human-robot interaction research.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

The human-robot interaction (HRI) field has traditionally used Wizard-of-Oz (WoZ) controlled robots to explore navigation, conversational dynamics, human-in-the-loop interactions, and more to explore appropriate robot behaviors in everyday settings. However, existing WoZ tools are often limited to one context, making them less adaptable across different settings, users, and robotic platforms. To mitigate these issues, we introduce a Context-Adaptable Robot Interface System (CARIS) that combines advanced robotic capabilities such teleoperation, human perception, human-robot dialogue, and multimodal data recording. Through pilot studies, we demonstrate the potential of CARIS to WoZ control a robot in two contexts: 1) mental health companion and as a 2) tour guide. Furthermore, we identified areas of improvement for CARIS, including smoother integration between movement and communication, clearer functionality separation, recommended prompts, and one-click communication options to enhance the usability wizard control of CARIS. This project offers a publicly available, context-adaptable tool for the HRI community, enabling researchers to streamline data-driven approaches to intelligent robot behavior.

Problem

• Addresses limitations of single-context Wizard-of-Oz robot interfaces
• Enables adaptable human-robot interaction across different settings and users
• Provides scalable multimodal control system for diverse robotic platforms

Innovation

• Context-adaptable interface system for HRI
• Combines teleoperation perception dialogue recording
• Enables WoZ control across multiple contexts