Walking ability is critically important for pediatric health, well-being, and independence. Children with cerebral palsy (CP), the most prevalent cause of pediatric physical disability, often present pathological gait patterns that negatively impact walking capacity. Reduced function of the muscles surrounding the ankle joint in those with CP also greatly increases the energy cost of transport leading to reduce mobility. Ankle-foot-orthoses show limited effectiveness for clinically relevant improvement in gait mechanics, while orthopedic surgery, muscle injections and physical therapy are unable to completely restore gait function. While wearable exoskeletons hold promise for gait rehabilitation, appropriately controlling the timing and magnitude of powered assistance across individuals and conditions remains a considerable challenge. This work seeks to address this challenge through the design and initial clinical verification of a simple ankle exoskeleton control scheme designed to reduce the metabolic cost of transport during walking in an individual with CP. Preliminary experimental results from instrumented gait analysis following 5 training visits demonstrated a 45% increase in positive ankle power and a 16% reduction in net metabolic rate during walking with the exoskeleton providing powered plantar-flexion assistance compared to walking without the exoskeleton. Future work will expand this investigation to a larger cohort of individuals with CP and across additional modes of locomotion.

Despite the classic nature of the problem, trajectory tracking for soft robots, i.e., robots with compliant elements deliberately introduced in their design, still presents several challenges. One of these is to design controllers which can obtain sufficiently high performance while preserving the physical characteristics intrinsic to soft robots. Indeed, classic control schemes using high-gain feedback actions fundamentally alter the natural compliance of soft robots effectively stiffening them, thus de facto defeating their main design purpose. As an alternative approach, we consider here using a low-gain feedback, while exploiting feedforward components. In order to cope with the complexity and uncertainty of the dynamics, we adopt a decentralized, iteratively learned feedforward action, combined with a locally optimal feedback control. The relative authority of the feedback and feedforward control actions adapts with the degree of uncertainty of the learned component. The effectiveness of the method is experimentally verified on several robotic structures and working conditions, including unexpected interactions with the environment, where preservation of softness is critical for safety and robustness.