论文标题
从基于骨架的观察结果中学习人体运动，以进行机器人辅助治疗
Learning Human Body Motions from Skeleton-Based Observations for Robot-Assisted Therapy
论文作者
论文摘要
在治疗方案中应用的机器人，例如，在患有自闭症谱系障碍的个体的治疗中，有时被用于模仿学习活动，其中一个人需要由机器人重复动作。为了简化合并机器人可以执行的新类型动作的任务，希望机器人能够通过观察人类（例如治疗师）的示威来学习动作。在本文中，我们研究了一种从人类的骨骼观察中获取动作的方法，该方法是由以机器人为中心的RGB-D相机收集的。给定一系列观察到各种关节，在通过PID位置控制器执行之前，将关节位置映射以匹配机器人的配置。我们通过使用Qtrobot进行一项研究来评估该方法，尤其是繁殖误差，其中机器人从多个参与者中获得了不同的上身舞蹈动作。结果表明该方法的总体可行性，但也表明繁殖质量受骨架观测中噪声的影响。
Robots applied in therapeutic scenarios, for instance in the therapy of individuals with Autism Spectrum Disorder, are sometimes used for imitation learning activities in which a person needs to repeat motions by the robot. To simplify the task of incorporating new types of motions that a robot can perform, it is desirable that the robot has the ability to learn motions by observing demonstrations from a human, such as a therapist. In this paper, we investigate an approach for acquiring motions from skeleton observations of a human, which are collected by a robot-centric RGB-D camera. Given a sequence of observations of various joints, the joint positions are mapped to match the configuration of a robot before being executed by a PID position controller. We evaluate the method, in particular the reproduction error, by performing a study with QTrobot in which the robot acquired different upper-body dance moves from multiple participants. The results indicate the method's overall feasibility, but also indicate that the reproduction quality is affected by noise in the skeleton observations.
