用大模型+机械臂帮上肢受限者增强社交肢体表达
Augmented Body Communicator: Enhancing daily body expression for people with upper limb limitations through LLM and a robotic arm
- 结合大语言模型与机械臂，实现上下文感知的动作建议
- 六名用户测试显示表达能力显著提升
- 适合残障人士社交辅助，也适用于人机协同设计
上肢活动受限者在人际互动中面临表达困难。尽管当前机械臂主要用于功能任务，但其在社交互动中增强身体语言的潜力尚未充分挖掘。本文提出一种增强型身体表达系统（Augmented Body Communicator），融合机械臂与大语言模型（LLM）。通过引入运动记忆机制，用户及其支持者可共同设计机械臂动作；在实际互动中，LLM根据上下文线索提供最适宜动作建议。六名上肢活动受限参与者参与了系统测试，结果表明该系统有效提升了用户的自我表达能力。基于研究发现，本文为开发兼具功能任务与身体语言支持能力的机械臂提供了实践建议。
原文摘要 · Abstract (English)
Individuals with upper limb movement limitations face challenges in interacting with others. Although robotic arms are currently used primarily for functional tasks, there is considerable potential to explore ways to enhance users' body language capabilities during social interactions. This paper introduces an Augmented Body Communicator system that integrates robotic arms and a large language model. Through the incorporation of kinetic memory, disabled users and their supporters can collaboratively design actions for the robot arm. The LLM system then provides suggestions on the most suitable action based on contextual cues during interactions. The system underwent thorough user testing with six participants who have conditions affecting upper limb mobility. Results indicate that the system improves users' ability to express themselves. Based on our findings, we offer recommendations for developing robotic arms that support disabled individuals with body language capabilities and functional tasks.
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