  To enable the broad adoption of wearable robotic exoskeletons in medical and
industrial settings, it is crucial they can adaptively support large
repertoires of movements. We propose a new human-machine interface to
simultaneously drive bilateral ankle exoskeletons during a range of 'unseen'
walking conditions and transitions that were not used for establishing the
control interface. The proposed approach used person-specific neuromechanical
models to estimate biological ankle joint torques in real-time from measured
electromyograms (EMGS) and joint angles. A low-level controller based on a
disturbance observer translated biological torque estimates into exoskeleton
commands. We call this 'neuromechanical model-based control' (NMBC). NMBC
enabled six individuals to voluntarily control a bilateral ankle exoskeleton
across six walking conditions, including all intermediate transitions, i.e.,
two walking speeds, each performed at three ground elevations, with no need for
predefined torque profiles, nor a priori chosen neuromuscular reflex rules, or
state machines as common in literature. A single subject case-study was carried
out on a dexterous locomotion tasks involving moonwalking. NMBC always enabled
reducing biological ankle torques, as well as eight ankle muscle EMGs both
within (22% torque; 12% EMG) and between walking conditions (24% torque; 14%
EMG) when compared to non-assisted conditions. Torque and EMG reductions in
novel walking conditions indicated that the exoskeleton operated symbiotically,
as exomuscles controlled by the operator's neuromuscular system. This opens new
avenues for the systematic adoption of wearable robots as part of
out-of-the-lab medical and occupational settings.
