使用人类示范和交互学习的上肢外骨骼机器人安全与个性化运动规划
Safe and Individualized Motion Planning for Upper-limb Exoskeleton Robots Using Human Demonstration and Interactive Learning
AI总结：
本文提出一种上肢外骨骼机器人运动规划方案，结合人类示范与交互学习，通过ProMP生成并迭代优化个性化轨迹，在可变阻抗模型下实现安全、定制化的康复辅助。
摘要：
上肢外骨骼机器人的典型应用是部署于康复训练中，帮助患者恢复操作能力。然而，由于患者并不总是能够跟随机器人，训练过程中可能会出现安全问题。由于不同患者之间存在差异，个性化的方案对于确保机器人适应患者的特定状况（例如运动习惯）也很重要，从而保证有效性。为了满足这一要求，本文提出了一种新的上肢外骨骼机器人运动规划方案，该方案利用人类示范和交互学习，驱动机器人提供定制化、安全和个性化的辅助。具体而言，机器人首先从一组健康受试者中学习，通过概率运动原语（ProMP）生成参考运动轨迹。然后，它在训练过程中从患者那里学习，以在移动安全区域内进一步塑造轨迹。交互数据被迭代地反馈到ProMP中，以在训练过程持续期间增强个性化特征。机器人在可变阻抗模型下跟踪个性化轨迹以实现辅助。最后，本文给出了实验结果以验证所提出的控制方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
