A roadmap for end-to-end task-agnostic exoskeleton control

Abstract

Lower-limb exoskeletons hold tremendous potential for augmenting human mobility across diverse applications, from assisting individuals with ageing-related mobility decline and disabilities to preventing workplace injuries in industrial settings. While human-in-the-loop optimization has driven many successful task-specific demonstrations, traditional control paradigms relying on discrete task classification face fundamental limitations: human movement is inherently continuous and infinitely variable, making task-specific approaches intractable for real-world deployment. This Perspective examines the shift towards end-to-end, artificial-intelligence-driven control systems that leverage real-time estimates of physiological state—specifically, biological joint moments. We discuss both recent advances and the key remaining challenges, including the need to optimize these systems, incorporate safety mechanisms and reduce the burden of collecting a suitable training dataset. Addressing these challenges while maintaining the flexibility and robustness of data-driven approaches will determine whether next-generation exoskeleton systems can meaningfully augment human mobility across diverse populations, environments and devices. Shepherd et al. propose end-to-end AI control of lower-limb exoskeletons based on real-time estimates of physiological signals.