Probabilistic movement primitive based motion learning for a lower limb exoskeleton with black-box optimization

As a wearable robot, an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer's movement with an anthropomorphic configuration. When an exoskeleton is used to facilitate the wearer's movement, a motion generation process often plays an important role in high-level control. One of the main challenges in this area is to generate in real time a reference trajectory that is parallel with human intention and can adapt to different situations. In this paper, we first describe a novel motion modeling method based on probabilistic movement primitive (ProMP) for a lower limb exoskeleton, which is a new and powerful representative tool for generating motion trajectories. To adapt the trajectory to different situations when the exoskeleton is used by different wearers, we propose a novel motion learning scheme based on black-box optimization (BBO) PIBB combined with ProMP. The motion model is first learned by ProMP offline, which can generate reference trajectories for use by exoskeleton controllers online. PIBB is adopted to learn and update the model for online trajectory generation, which provides the capability of adaptation of the system and eliminates the effects of uncertainties. Simulations and experiments involving six subjects using the lower limb exoskeleton HEXO demonstrate the effectiveness of the proposed methods.