Yanhe Chen
Yanhe is is a Software Engineer at Google, dedicated to building innovative, user-centric products at the intersection of artificial intelligence and user experience.
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Sensible Agent: A Framework for Unobtrusive Interaction with Proactive AR Agent
Min Xia
Nels Numan
Dinesh Manocha
Proceedings of the 39th Annual ACM Symposium on User Interface Software and Technology (UIST), ACM (2025), pp. 22
Preview abstract
Proactive AR agents promise context-aware assistance, but their interactions often rely on explicit voice prompts or responses, which can be disruptive or socially awkward. We introduce Sensible Agent, a framework designed for unobtrusive interaction with these proactive agents. Sensible Agent dynamically adapts both “what” assistance to offer and, crucially, “how” to deliver it, based on real-time multimodal context sensing. Informed by an expert workshop (n=12) and a data annotation study (n=40), the framework leverages egocentric cameras, multimodal sensing, and Large Multimodal Models (LMMs) to infer context and suggest appropriate actions delivered via minimally intrusive interaction modes. We demonstrate our prototype on an XR headset through a user study (n=10) in both AR and VR scenarios. Results indicate that Sensible Agent significantly reduces perceived intrusiveness and interaction effort compared to voice-prompted baseline, while maintaining high utility.
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XR Blocks: Accelerating Human-Centered AI + XR Innovation
Nels Numan
Evgenii Alekseev
Alex Cooper
Min Xia
Scott Chung
Jeremy Nelson
Xiuxiu Yuan
Jolica Dias
Tim Bettridge
Benjamin Hersh
Michelle Huynh
Konrad Piascik
Ricardo Cabello
Google, XR, XR Labs (2025)
Preview abstract
We are on the cusp where Artificial Intelligence (AI) and Extended Reality (XR) are converging to unlock new paradigms of interactive computing. However, a significant gap exists between the ecosystems of these two fields: while AI research and development is accelerated by mature frameworks like PyTorch and benchmarks like LMArena, prototyping novel AI-driven XR interactions remains a high-friction process, often requiring practitioners to manually integrate disparate, low-level systems for perception, rendering, and interaction. To bridge this gap, we present XR Blocks, a cross-platform framework designed to accelerate human-centered AI + XR innovation. XR Blocks provides a modular architecture with plug-and-play components for core abstraction in AI + XR: user, world, peers; interface, context, and agents. Crucially, it is designed with the mission of "minimum code from idea to reality", accelerating rapid prototyping of complex AI + XR apps. Built upon accessible technologies (WebXR, three.js, TensorFlow, Gemini), our toolkit lowers the barrier to entry for XR creators. We demonstrate its utility through a set of open-source templates, samples, and advanced demos, empowering the community to quickly move from concept to interactive prototype.
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