Wearable robots aim to seamlessly adapt to humans and their environment with personalized interactions. Existing supernumerary robotic limbs (SRLs), which enhance the physical capabilities of humans with additional extremities, have thus far been developed primarily for task-specific applications in structured industrial settings, limiting their adaptability to dynamic and unstructured environments. Here, we introduce a novel reconfigurable SRL framework grounded in a quantitative analysis of human augmentation to guide the development of more adaptable SRLs for diverse scenarios. This framework captures how SRL configuration shapes workspace extension and human-robot collaboration. We define human augmentation ratios to evaluate collaborative, visible extended, and non-visible extended workspaces, enabling systematic selection of SRL placement, morphology, and autonomy for a given task. Using these metrics, we demonstrate how quantitative augmentation analysis can guide the reconfiguration and control of SRLs to better match task requirements. We validate the proposed approach through experiments with a reconfigurable SRL composed of origami-inspired modular elements. Our results suggest that reconfigurable SRLs, informed by quantitative human augmentation analysis, offer a new perspective for providing adaptable human augmentation and assistance in everyday environments.

翻译：可穿戴机器人旨在通过个性化交互无缝适应人类及其环境。现有超数机器肢体通过额外肢体增强人类体能，目前主要针对结构化工业场景中的特定任务应用，限制了其在动态非结构化环境中的适应性。本文提出一种基于人体增强量化分析的新型可重构超数机器肢体框架，以指导开发更具适应性的超数机器肢体应用于多样化场景。该框架揭示了超数机器肢体构型如何影响工作空间扩展与人机协作。我们定义人体增强比率以评估协作、可视扩展与不可视扩展工作空间，从而针对给定任务系统选择超数机器肢体的安装位置、形态与自主性。通过量化增强分析，我们证明这些指标可指导超数机器肢体重构与控制以更好匹配任务需求。采用折纸启发模块化元件组成的可重构超数机器肢体进行实验验证。结果表明，基于量化人体增强分析的可重构超数机器肢体为日常环境中提供适应性人体增强与辅助提供了新视角。