Search arXivSearch

arXiv · 2508.04108

XARP Tools: An Extended Reality Platform for Humans and AI Agents

Abstract

Building XR-AI research prototypes requires navigating two largely separate ecosystems. Mainstream XR development relies on C#/C++ and game engines, while AI development is centered on Python. This toolchain fragmentation slows down contributions to human-AI spatial interaction research. To broaden access to XR development in the Python ecosystem, we present XARP (XR Agent-ready Remote Procedures), a toolkit for rapid XR-AI prototyping in Python. XARP application logic runs on a Python server and controls a Unity client through WebSocket messages. This architecture enables compatibility with multiple client platforms and live reloading of application code without client redeployment. XARP is available to humans as a library and to AI agents as callable tools and through Model Context Protocol. We designed XARP through formative case studies and refined it through an early acceptance evaluation with 24 XR and AI developers and a six-week longitudinal study with two developers building an independent research project. Potential users expected the toolkit to improve their performance and facilitate development. Sustained use confirmed faster iteration and easier setup compared to conventional XR workflows, with asset-intensive and performance-critical projects emerging as the clearest limitations. Technical benchmarks show that hand and head tracking data streaming was close to the device refresh rate of 72 FPS, and that AI agents using XARP consumed 19% fewer tokens than those writing equivalent C# Unity code. Beyond broadening access to XR development, XARP reduces engineering friction in spatial computing research and opens new pathways for AI agents to participate in XR application development. XARP is open source and available at https://github.com/hal-ucsb/xarp.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Arthur Caetano, Radha Kumaran, Kelvin Jou, Tobias Höllerer, Misha Sra. 2026-05-21. XARP Tools: An Extended Reality Platform for Humans and AI Agents. https://doi.org/10.1145/3816762

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

cs.HC

Mirror Skin: In Situ Visualization of Robot Touch Intent on Robotic Skin

Effective communication of robot touch intent is essential for safe and predictable physical human-robot interaction. While intent communication has been widely studied, existing approaches lack the spatial specificity and semantic depth necessary to efficiently convey robot touch intent. We present Mirror Skin, a cephalopod inspired concept that mirrors in-situ visual representations of a human's body parts onto the corresponding robot's touch region to communicate who shall initiate touch, where it will occur, and when it is imminent. We informed the design of Mirror Skin through a structured design exploration with experts and demonstrate the real-world feasibility of Mirror Skin with a proof-of-concept prototype. User studies in VR and with the physical prototype showed that Mirror Skin significantly improves accuracy and response times for interpreting touch intent and improves the user experience during physical human-robot interactions.

cs.HC

VisCanvas: A Node-Based Interface for Exploratory Visualization Authoring with LLMs

Visual data analysis involves both open-ended exploration and targeted question answering. Visualization authoring tools support this process by enabling users to create visualizations for these tasks. With the rise of large language models (LLMs), substantial effort has been devoted to developing visualization authoring tools that use natural language instructions. However, existing systems are typically based on a linear chat interface, which is not well suited to exploratory visual analysis workflows. In this paper, we introduce VisCanvas, a node-based interface for exploratory visualization authoring with LLMs. VisCanvas allows users to create, revise, branch, and merge visualizations in a non-linear way, enabling more efficient exploration of multiple analytical directions. We conducted a user study with 20 participants to evaluate the effectiveness of VisCanvas compared to a baseline chat-based interface. The results show that VisCanvas facilitates more diverse data interaction while maintaining performance levels (i.e., cognitive load and usability) that are indistinguishable from current prevailing methods. We then distill design principles for future AI-assisted visualization authoring environments. All supplemental materials required to reproduce the study are available at https://osf.io/gsxhn.

cs.HC