The characteristics of human-robot coadaptation during human-in-the-loop optimization of exoskeleton control

Human-in-the-loop (HITL) optimization of exoskeleton control during assisted walking can improve human mobility and reduce the energy cost. This process involves human-robot coadaptation as suggested by prior studies. There was a drop in the same subjects metabolic cost under the same assisted walking condition before and after the optimization process. It means the subjects adapted to walking with the exoskeleton while the exoskeleton learned the optimal control parameters for the subjects. We analyzed the process of human bodies learning to walk with an ankle exoskeleton, aiming to quantify the characteristics of human-robot coadaptation during HITL optimization of exoskeleton control. Data of eleven participants from prior experiments were utilized in this study. We identified similar sample conditions for each participant and investigated the trend of metabolic cost along with the HITL exoskeleton control optimization process. Results showed that the relationship between human metabolic cost and the time past in the optimization cycle approximately followed exponential curves with widespread adaptation rates. For the optimization process of four parameters with each condition sampled for two minutes, the time constants were averaged at 238 ± 207 optimization sample conditions. Our results can provide guidance to the training process of robot assisted human motion.