arXiv:2602.15707v2 Announce Type: replace-cross Abstract: Real-time conversational assistants for procedural manual tasks often depend on video input, which can be computationally expensive and compromise user privacy. For the first time, we propose a real-time conversational assistant that provides comprehensive guidance for procedural manual tasks using only lightweight privacy-preserving modalities such as audio and IMU inputs from a user's wearable device to understand the context. Using a furniture assembly task and a cooking task, we show how this assistant proactively communicates step-
Advances in AI, particularly in audio and IMU processing, combined with the increasing demand for user-friendly and privacy-preserving interfaces, enable the development of such conversational assistants now.
This development could significantly broaden the application of AI assistants in real-world environments, particularly in sensitive or physically demanding tasks, by offering a more private and efficient interaction model.
The reliance on computationally intensive and privacy-invasive video input for procedural task guidance is reduced, paving the way for more ubiquitous and acceptable AI assistance in human-centric activities.
- · Wearable tech manufacturers
- · AI assistant developers
- · Individuals requiring procedural guidance
- · Privacy-focused tech companies
- · Traditional video-based assistance systems
- · Companies reliant on pervasive visual data collection
Increased adoption of AI assistants in industrial, home, and educational settings for hands-on tasks.
Reduced user friction for complex manual tasks, potentially boosting productivity and reducing errors in fields like manufacturing or maintenance.
The development of a new standard for human-AI interaction that prioritizes privacy and efficiency over comprehensive visual data collection.
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