A typical application of upper-limb exoskeleton robots is deployment in rehabilitation training, helping patients to regain manipulative abilities. However, as the patient is not always capable of following the robot, safety issues may arise during the training. Due to the bias in different patients, an individualized scheme is also important to ensure that the robot suits the specific conditions (e.g., movement habits) of a patient, hence guaranteeing effectiveness. To fulfill this requirement, this paper proposes a new motion planning scheme for upper-limb exoskeleton robots, which drives the robot to provide customized, safe, and individualized assistance using both human demonstration and interactive learning. Specifically, the robot first learns from a group of healthy subjects to generate a reference motion trajectory via probabilistic movement primitives (ProMP). It then learns from the patient during the training process to further shape the trajectory inside a moving safe region. The interactive data is fed back into the ProMP iteratively to enhance the individualized features for as long as the training process continues. The robot tracks the individualized trajectory under a variable impedance model to realize the assistance. Finally, the experimental results are presented in this paper to validate the proposed control scheme.

翻译：上肢外骨骼机器人的典型应用场景是康复训练，帮助患者恢复操作能力。然而，由于患者并非总能跟随机器人运动，训练过程中可能出现安全问题。此外，不同患者之间存在个体差异，因此制定个性化方案以确保机器人适应患者的具体状况（如运动习惯）至关重要，这直接关系到康复效果。为实现这一目标，本文提出了一种适用于上肢外骨骼机器人的新型运动规划方案，该方案通过融合人类示教与交互学习，驱动机器人提供定制化、安全且个性化的辅助。具体而言，机器人首先从一组健康受试者的示范中学习，通过概率运动基元（ProMP）生成参考运动轨迹；随后在训练过程中通过与患者的交互进一步优化轨迹，使其始终处于动态安全区域内。交互数据被持续反馈回ProMP模型，随着训练进程不断迭代增强个性化特征。机器人采用可变阻抗模型对个性化轨迹进行跟踪，从而实现辅助功能。最后，本文通过实验验证了所提控制方案的有效性。