1. Experimental setup for HIL Bayesian optimization.
Experimental setup for HIL Bayesian optimization. Bayesian optimization was used to adjust the control parameters of an assistive device to minimize the metabolic cost of walking. The metabolic rate was estimated from respiratory measurements and used to compute a posterior distribution of metabolic rate with respect to the free control parameters. The posterior was initially generated by evaluating six prefixed control parameters. Given the posterior at the current iteration, the control parameters with maximum EI were chosen and applied to the wearable device. This process was repeated until convergence. During this process, the configured force profiles were delivered through a soft hip exosuit with a tethered actuation system.
Ye Ding et al. Sci. Robotics 2018;3:eaar5438
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