To enable the broad adoption of wearable robotic exoskeletons in medical and industrial settings, it is crucial they can effectively support large repertoires of movements. We propose a new human-machine interface to drive bilateral ankle exoskeletons during a range of 'unseen' walking conditions that were not used for establishing the control interface. The proposed approach uses person-specific neuromechanical models of the human body to estimate biological ankle torques in real-time from electromyograms (EMGS) and joint angles. A low-level controller based on a disturbance observer translates biological torque estimates into exoskeleton commands. We call this 'neuromechanical model-based control' (NMBC). NMBC enabled five individuals to voluntarily control exoskeletons across two walking speeds performed at three ground elevations with no need for predefined torque profiles, nor a prior chosen neuromuscular reflex rules, or state machines as common in literature. Furthermore, a single subject case study was carried out on a dexterous moonwalk task, showing reduction in muscular effort. NMBC enabled reducing biological ankle torques as well as eight ankle muscle EMGs both within (22% for the torque; 13% for the EMG) and between walking conditions (22% for the torque; 13% for the EMG) when compared to non-assisted conditions. Torque and EMG reduction in novel walking conditions indicated the exoskeleton operated symbiotically as an exomuscle controlled by the operator's neuromuscular system. This will open new avenues for systematic adoption of wearable robots in out-of-the-lab medical and occupational settings.