VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots (2404.04066v2)

Abstract: Physically assistive robots present an opportunity to significantly increase the well-being and independence of individuals with motor impairments or other forms of disability who are unable to complete activities of daily living. Speech interfaces, especially ones that utilize LLMs, can enable individuals to effectively and naturally communicate high-level commands and nuanced preferences to robots. Frameworks for integrating LLMs as interfaces to robots for high level task planning and code generation have been proposed, but fail to incorporate human-centric considerations which are essential while developing assistive interfaces. In this work, we present a framework for incorporating LLMs as speech interfaces for physically assistive robots, constructed iteratively with 3 stages of testing involving a feeding robot, culminating in an evaluation with 11 older adults at an independent living facility. We use both quantitative and qualitative data from the final study to validate our framework and additionally provide design guidelines for using LLMs as speech interfaces for assistive robots. Videos and supporting files are located on our project website: https://sites.google.com/andrew.cmu.edu/voicepilot/

• The paper presents a novel framework that integrates LLMs as speech interfaces for assistive robots, validated through iterative empirical evaluations in real-world feeding tasks.
• The authors employed iterative testing phases, combining qualitative and quantitative feedback to ensure customization, multi-step command execution, and consistent performance.
• The study demonstrates that intuitive LLM-driven interfaces can deliver safe, efficient, and socially engaging support, matching caregiver execution times in assistive scenarios.

VoicePilot: LLMs as Speech Interfaces for Assistive Robots

The intersection of robotics and AI through the integration of LLMs presents transformative potential in assistive technology. The paper "VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots" explores this frontier by developing a framework for utilizing LLMs as speech interfaces for robots designed to assist individuals with disabilities.

Framework Development and Iteration

The authors propose a comprehensive framework for integrating LLMs into assistive robots, driven by the need for human-centric considerations absent in prior models. This framework is iteratively refined through several empirical phases, focusing on the robot-assisted feeding domain with the Obi feeding robot. The significant elements of this framework include Environment Description, Robot Functions, Function Applications, Code Specifications, Safety, Robot Variables, and additional considerations like Instructional Materials, User Control Functions, and Feedback.

Empirical Evaluation

The approach is evaluated through three stages of testing: initial piloting with lab members, a demonstration with community engagement, and a formal paper involving older adults at an independent living facility. The blending of qualitative and quantitative data validates the framework and informs the iterative enhancement of the LLM integration.

Results and Findings

The paper demonstrates favorable outcomes in terms of usability and acceptance, with participants, especially older adults, finding the speech interface intuitive and effective for executing basic and customized feeding tasks. The authors emphasize the relevance of customization, multi-step instruction, execution consistency, social capability, and ensuring comparable execution time to human caregivers. Despite high variance in user satisfaction due to individual customization and variability in LLM processing, the framework's adaptability and robustness in real-world scenarios indicate promising avenues for further exploration and enhancement.

Design Guidelines

The paper establishes critical design guidelines derived from thematic analysis of participant interactions and feedback:

1. Customization: Users must be able to personalize commands to their preferences, fostering a sense of control over the assistive technology.
2. Multi-Step Instruction: Allowing users to issue complex commands encompassing sequential tasks enhances efficiency and user experience.
3. Consistency: Consistent performance from the interface in command recognition and execution builds user trust and reliability.
4. Comparable Time to Caregiver: The importance of executing tasks in a timeframe similar to human caregivers cannot be understated, promoting user comfort and efficiency.
5. Social Capability: Inclusion of conversational elements enhances user engagement, especially for those who seek companionship in assistive settings.

Implications and Future Work

This work significantly contributes to the field of assistive robotics by not only innovating a technical framework but also prioritizing user-centric design principles, emphasizing customization, and adaptability across diverse user needs. The implications for AI and robotics extend beyond assisted feeding, potentially transforming how LLMs are utilized for diverse assistive functions.

Future research could encompass broader testing across various assistive robots and with diverse user groups, including those with significant motor impairments, to validate and refine the proposed system further. Moreover, exploring newer LLM architectures and incorporating advanced customization could alleviate some of the current variability issues, offering even greater consistency and satisfaction.