  One of the typical purposes of using lower-limb exoskeleton robots is to
provide assistance to the wearer by supporting their weight and augmenting
their physical capabilities according to a given task and human motion
intentions. The generalizability of robots across different wearers in multiple
tasks is important to ensure that the robot can provide correct and effective
assistance in actual implementation. However, most lower-limb exoskeleton
robots exhibit only limited generalizability. Therefore, this paper proposes a
human-in-the-loop learning and adaptation framework for exoskeleton robots to
improve their performance in various tasks and for different wearers. To suit
different wearers, an individualized walking trajectory is generated online
using dynamic movement primitives and Bayes optimization. To accommodate
various tasks, a task translator is constructed using a neural network to
generalize a trajectory to more complex scenarios. These generalization
techniques are integrated into a unified variable impedance model, which
regulates the exoskeleton to provide assistance while ensuring safety. In
addition, an anomaly detection network is developed to quantitatively evaluate
the wearer's comfort, which is considered in the trajectory learning procedure
and contributes to the relaxation of conflicts in impedance control. The
proposed framework is easy to implement, because it requires proprioceptive
sensors only to perform and deploy data-efficient learning schemes. This makes
the exoskeleton practical for deployment in complex scenarios, accommodating
different walking patterns, habits, tasks, and conflicts. Experiments and
comparative studies on a lower-limb exoskeleton robot are performed to
demonstrate the effectiveness of the proposed framework.
