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Lower-body exoskeleton control that adapts to users and provides assistance-as-needed can increase user participation and motor learning and allow for more effective gait rehabilitation. Adaptive model-based control methods have previously been developed to consider a user’s interaction with an exoskeleton; however, the predefined dynamics models required are challenging to define accurately, due to the complex dynamics and nonlinearities of the human-exoskeleton interaction. Model-free deep reinforcement learning (DRL) approaches can provide accurate and robust control in robotics applications and have shown potential for lower-body exoskeletons. In this paper, we present a new model-free DRL method for end-to-end learning of desired gait patterns for over-ground gait rehabilitation with an exoskeleton. This control technique is the first to accurately track any gait pattern desired in physiotherapy without requiring a predefined dynamics model and is robust to varying post-stroke individuals’ baseline gait patterns and their interactions and perturbations. Simulated experiments of an exoskeleton paired to a musculoskeletal model show that the DRL method is robust to different post-stroke users and is able to accurately track desired gait pattern trajectories both seen and unseen in training.

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Stroke is a leading cause of disability, and robotic lower body exoskeletons have been developed to aid in gait rehabilitation. The simulation modeling and testing processes are often developed and deployed separately. This introduces additional steps which can hinder on-the-fly customization of gait patterns required for individualized gait rehabilitation. In this paper, we present a centralized control architecture which integrates both the simulated model and the exoskeleton hardware for lower body exoskeletons. The architecture allows for ease of simulating, adapting, and deploying gait patterns on an exoskeleton for use in gait rehabilitation, and allows for the on-the-fly customization and verification of gait patterns by physiotherapists during rehabilitation. Experiments validate the use of our overall control architecture to both model and control a physical exoskeleton, while following desired gait patterns.

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