The high energy cost of walking in individuals with cerebral palsy (CP) contributes significantly to reduced mobility and quality of life. The purpose of this paper was to develop and clinically evaluate an untethered ankle exoskeleton with the ability to reduce the metabolic cost of walking in children and young adults with gait pathology from CP. We designed a battery-powered device consisting of an actuator-and-control module worn above the waist with a Bowden cable transmission used to provide torque to pulleys aligned with the ankle. Special consideration was made to minimize adding mass to the body, particularly distal portions of the lower-extremity. The exoskeleton provided plantar-flexor assistance during the mid-to-late stance phase, controlled using a real-time control algorithm and embedded sensors. We conducted a device feasibility and a pilot clinical evaluation study with five individuals with CP ages five through thirty years old. Participants completed an average of 130 min of exoskeleton-assisted walking practice. We observed a 19±5% improvement in the metabolic cost of transport (p = 0.011) during walking with untethered exoskeleton assistance compared to how participants walked normally. These preliminary findings support the future investigation of powered ankle assistance for improving mobility in this patient population.

Soft robots are one of the most significant recent evolutions in robotics. They rely on compliant physical structures purposefully designed to embody desired characteristics. Since their introduction, they have shown remarkable applicability in overcoming their rigid counterparts in such areas as interaction with humans, adaptability, energy efficiency, and maximization of peak performance. Nonetheless, we believe that research on novel soft robot applications is still slowed by the difficulty in obtaining or developing a working soft robot structure to explore novel applications

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 highgain 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.