Abstract
Supernumerary robotic limbs (SLs) have the potential to extend human sensorimotor capabilities by increasing effective degrees of freedom. However, current systems are designed for a single task and cannot flexibly coordinate with natural limbs (NLs). Here we propose a hierarchical control architecture for human–SL augmentation that draws on predictive coding to couple intent inference, safety, action planning, and interaction. The top layer infers user goals and imposes task- and environment-level constraints to guarantee dynamic safety and multilimb coordination. A mid layer plans actions and synthesizes multisensory feedback for the user, while a low layer executes compliant interaction with impedance control and augmented sensory cues. Central to the framework is General Voluntary Control (GVC), a novel interaction mechanism that integrates autonomous SL behaviors with direct human commands. GVC treats the human and SLs as agents engaged in a differential game, sharing motion plans and allocating effort according to (i) a parameter capturing
trust
in the human plan, and (ii) an
effort-sharing
parameter that distributes task load. The GVC is validated through simulations of human-SLs object co-manipulation. Across a wide range of movement durations, loads, prediction errors, and diverging motion plans, the controller remains stable, shares effort appropriately, and maintains low tracking error. Human-like impedance adaptation further reduces competition and improves convergence. The results provide design principles for safe, versatile, and intuitive SL control in industrial, assistive, and surgical settings.