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EmBARDiment: an Embodied AI Agent for Productivity in XR

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arxiv 2408.08158 v2 pith:WZRR5DN5 submitted 2024-08-15 cs.HC cs.MA

Riccardo Bovo , Steven Abreu , Karan Ahuja , Eric J Gonzalez , Li-Te Cheng , Mar Gonzalez-Franco This is my paper · ORCID
classification cs.HCcs.MA
keywords chat-botsdataexplicitproductivitypromptsuseractionsadvantage
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XR devices running chat-bots powered by Large Language Models (LLMs) have the to become always-on agents that enable much better productivity scenarios. Current screen based chat-bots do not take advantage of the the full-suite of natural inputs available in XR, including inward facing sensor data, instead they over-rely on explicit voice or text prompts, sometimes paired with multi-modal data dropped as part of the query. We propose a solution that leverages an attention framework that derives context implicitly from user actions, eye-gaze, and contextual memory within the XR environment. Our work minimizes the need for engineered explicit prompts, fostering grounded and intuitive interactions that glean user insights for the chat-bot.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reality Proxy: Fluid Interactions with Real-World Objects in MR via Abstract Representations

    cs.HC 2025-07 conditional novelty 6.0 of 10

    Reality Proxy replaces direct selection of physical objects in MR with AI-enriched, hand-placed abstract proxies that support skimming, multi-selection, filtering, and semantic grouping.

  2. Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches

    cs.AI 2025-01 conditional novelty 3.0 of 10

    This survey argues that embodiment, symbol grounding, causality, and memory are the foundational principles needed to make large language models achieve artificial general intelligence.

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