Reconfiguration of supernumerary robotic limbs for human augmentation

Abstract

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.

Figures (14)

• Figure 1: Human augmentation with reconfigurable supernumerary robotic limbs. Human augmentation through supernumerary robotic limbs (SRLs) has the potential to provide personalized assistance across diverse scenarios. SRLs enhance human manipulation capabilities by enabling tasks such as object stabilization when the user’s arms are occupied. They support dentists and surgeons by facilitating multi-arm procedures and improving productivity in industrial contexts, such as crop planting. Reconfigurable SRLs adapt their morphology, placement, and level of autonomy to the task and scenario, providing assistance that ranges from manual to fully autonomous task execution.
• Figure 1: Robogami module. The Robogami module realizes a parallel kinematic structure based on the Canfield joint principle through the use of three origami-inspired folding legs. Each of the legs is connected to a DC motor to realize the actuation of the module. Two versions of the Robogami module are used: (A) A strong but heavier version using a motor with 1.4 torque and a weight of 57g. (B) A weaker but lighter version using a motor with 0.52 torque and a weight of 18g.
• Figure 2: Human augmentation analysis. Different tasks and scenarios require varying configurations of SRLs. This analysis quantifies the impact of varying placement $p_j$ and morphology (number of modules $n$) of the SRL, as well as the human visual field $V$ on human augmentation. (A) The 4 different SRL configurations on the human body, which capture the range of potential augmentation tasks enabled by the SRL. The SRL is placed on the chest $p_1$, back $p_2$, front of the leg $p_3$, and side of the leg $p_4$. (B) The human augmentation ratios as a function of the number of modules $n$ for the 4 SRL configurations: Extension ratio $r_e$, visible extension ratio $r_{e,v}$, and collaboration ratio $r_c$.
• Figure 2: Structural reconfiguration of Robogami module. The parallel kinematic structure of the Robogami module enables reconfiguration from a shape with a 4cm height (A) to a shape with a 14cm height (B). This results in an extension ratio of $3.5$ for a single Robogami module.
• Figure 3: Workspace analysis to inform the reconfiguration strategy. The SRL configuration, defined by the placement $p$ and the number of modules $n$, must be selected according to the scenario and task requirements. Workspace analysis supports this selection by computing workspace ratios $w_{i,n}$ for the collaborative workspace ($C$), the visible extended workspace ($E_{\text{V}}$), and the non-visible extended workspace ($E_{\text{NV}}$). The 4 SRL configurations ($p_1$-$p_4$) on the human body, that capture the range of potential augmentation tasks enabled by the SRL, are analyzed (A-D).