The integration of manipulator robots in household environments suggests a need for more predictable and human-like robot motion. This holds especially true for wheelchair-mounted assistive robots that can support the independence of people with paralysis. One method of generating naturalistic motion trajectories is via the imitation of human demonstrators. This paper explores a self-supervised imitation learning method using an autoregressive spatio-temporal graph neural network for an assistive drinking task. We address learning from diverse human motion trajectory data that were captured via wearable IMU sensors on a human arm as the action-free task demonstrations. Observed arm motion data from several participants is used to generate natural and functional drinking motion trajectories for a UR5e robot arm.

翻译：将机械臂机器人融入家庭环境的需求促使机器人运动更具可预测性和类人特征，这对于安装在轮椅上、帮助瘫痪患者实现独立生活的辅助机器人尤为重要。生成自然运动轨迹的方法之一是通过模仿人类示范者的动作。本文探讨了一种自监督模仿学习方法，利用自回归时空图神经网络进行辅助饮水任务。我们针对从人类手臂可穿戴惯性测量单元传感器捕获的多样化运动轨迹数据（作为无动作标记的任务演示）进行学习。通过多名参与者观测到的手臂运动数据，为UR5e机器人手臂生成了自然且具备功能性的饮水运动轨迹。