Continual learning offers a principled approach for enabling adaptive systems to learn from ongoing experience while retaining previously acquired knowledge, a capability that is central to intelligent behavior in natural systems. However, its application to the control of safety-critical, human-in-the-loop robotic systems remains limited. Assistive exoskeletons, particularly soft wearable exosuits, provide a compelling and practically relevant testbed for advancing continual learning in such settings. Soft exoskeletons, or exosuits, offer a promising means to enhance human motor performance, reduce physical workload, and support rehabilitation by employing compliant materials that conform to the human body, preserve natural motion, and improve comfort and usability [1–4]. A fundamental challenge in exosuit control is that the interaction dynamics between the human and the exosuit are inherently nonstationary and continuously evolving. Changes in task objectives, external loads, posture, and fatigue can significantly alter the effective behavior of the coupled human–device system. These changes may occur gradually or abruptly and often without clearly defined task boundaries, making it difficult to predefine operating conditions or rely on fixed control strategies. As a result, controllers optimized for a single task or operating condition may exhibit degraded performance or unsafe behavior when deployed across varying conditions [5,6]. This motivates the need for adaptive control strategies that can learn continuously while retaining previously effective behaviors. Recent work has demonstrated the potential of continual learning for adaptive control of soft robotic systems, for example, by enabling neural-network-based controllers to adapt to changing external loads without catastrophic forgetting [7]. However, the application of continual learning to human–exosuit interaction, where learning must operate under physical interaction, user variability, and safety constraints, remains largely unexplored. To address this gap, this project aims to develop a unified framework for continual learning in human–robot interaction. An integrated soft wearable exosuit and musculoskeletal human model will serve as a human-in-the-loop evaluation platform. Specifically, the project leverages OpenSim-based musculoskeletal simulation [8] to model upper-body human dynamics coupled with a wearable exosuit and formulates exosuit assistance as a continual learning problem. OpenSim has been extensively used in prior studies to model assistive devices and to investigate their interaction with the human musculoskeletal system [9,10].
