Proactive Conversational Assistant for a Procedural Manual Task based on Audio and IMU
arXiv:2602.15707v1 Announce Type: cross Abstract: Real-time conversational assistants for procedural 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 a procedural task using only lightweight privacy-preserving modalities such as audio and IMU inputs from a user's wearable device to understand the context. This assistant proactively communicates step-by-step instructions to a user performing a furniture assembly task, and answers user questions. We construct a dataset containing conversations where the assistant guides the user in performing the task. On observing that an off-the-shelf language model is a very talkative assistant, we design a novel User Whim Agnostic (UWA) LoRA finetuning method which improves the model's ability to suppress less informative dialogues, while maintaining its tendency to communicate important i
arXiv:2602.15707v1 Announce Type: cross Abstract: Real-time conversational assistants for procedural 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 a procedural task using only lightweight privacy-preserving modalities such as audio and IMU inputs from a user's wearable device to understand the context. This assistant proactively communicates step-by-step instructions to a user performing a furniture assembly task, and answers user questions. We construct a dataset containing conversations where the assistant guides the user in performing the task. On observing that an off-the-shelf language model is a very talkative assistant, we design a novel User Whim Agnostic (UWA) LoRA finetuning method which improves the model's ability to suppress less informative dialogues, while maintaining its tendency to communicate important instructions. This leads to >30% improvement in the F-score. Finetuning the model also results in a 16x speedup by eliminating the need to provide in-context examples in the prompt. We further describe how such an assistant is implemented on edge devices with no dependence on the cloud.
Executive Summary
This article presents a novel approach to developing a conversational assistant for procedural tasks using audio and IMU inputs from a user's wearable device. The proposed assistant proactively communicates step-by-step instructions to the user, answering their questions in real-time. The authors design a novel User Whim Agnostic (UWA) LoRA finetuning method to improve the model's ability to suppress less informative dialogues, resulting in a >30% improvement in F-score. The assistant is implemented on edge devices with no dependence on the cloud, achieving a 16x speedup. This breakthrough has significant implications for user privacy and efficiency in procedural tasks.
Key Points
- ▸ The proposed conversational assistant uses lightweight privacy-preserving modalities such as audio and IMU inputs.
- ▸ The assistant proactively communicates step-by-step instructions to the user, answering their questions in real-time.
- ▸ The authors design a novel UWA LoRA finetuning method to improve the model's ability to suppress less informative dialogues.
- ▸ The assistant is implemented on edge devices with no dependence on the cloud, achieving a 16x speedup.
Merits
Improved User Experience
The proposed assistant provides comprehensive guidance for procedural tasks, enhancing user experience and efficiency.
Enhanced User Privacy
The use of audio and IMU inputs from a user's wearable device preserves user privacy, eliminating the need for video input.
Increased Efficiency
The assistant is implemented on edge devices, achieving a 16x speedup and reducing computational expenses.
Demerits
Limited Task Domain
The proposed assistant is designed for a specific task (furniture assembly), limiting its applicability to other domains.
Dependence on Wearable Devices
The assistant requires wearable devices with audio and IMU inputs, which may not be universally available.
Expert Commentary
The proposed conversational assistant presents a significant breakthrough in developing real-time, user-friendly, and privacy-preserving AI solutions. The design of the UWA LoRA finetuning method demonstrates a novel approach to improving conversational AI efficiency. However, the assistant's limited task domain and dependence on wearable devices are notable limitations. As the assistant is implemented on edge devices, it may influence the development of edge AI and raise important questions about data protection and AI development. Further research is needed to expand the assistant's applicability to other domains and ensure its widespread adoption.
Recommendations
- ✓ Future research should focus on expanding the assistant's task domain and developing a more versatile conversational AI framework.
- ✓ Policymakers and industry leaders should consider the assistant's implications for user privacy and data protection, influencing policy decisions accordingly.
