Accurate prediction of human movements is required to enhance the efficiency of physical human-robot interaction. Behavioral differences across various users are crucial factors that limit the prediction of human motion. Although recent neural network-based modeling methods have improved their prediction accuracy, most did not consider an effective adaptations to different users, thereby employing the same model parameters for all users. To deal with this insufficiently addressed challenge, we introduce a meta-learning framework to facilitate the rapid adaptation of the model to unseen users. In this study, we propose a model structure and a meta-learning algorithm specialized to enable fast user adaptation in predicting human movements in cooperative situations with robots. The proposed prediction model comprises shared and adaptive parameters, each addressing the user's general and individual movements. Using only a small amount of data from an individual user, the adaptive parameters are adjusted to enable user-specific prediction through a two-step process: initialization via a separate network and adaptation via a few gradient steps. Regarding the motion dataset that has 20 users collaborating with a robotic device, the proposed method outperforms existing meta-learning and non-meta-learning baselines in predicting the movements of unseen users.

翻译：需要准确预测人类运动,以提高人体-机器人物理互动的效率。不同用户之间的行为差异是限制人类运动预测的关键因素。尽管最近的神经网络模型方法提高了预测准确性,但大多数没有考虑对不同用户进行有效的调整,因此没有考虑对所有用户采用同样的模型参数。为了应对这一未得到充分应对的挑战,我们引入了元学习框架,以便利该模型迅速适应隐蔽用户。在本研究中,我们提议了一个模型结构和元学习算法,专门使用户能够快速适应预测与机器人合作情况下人类运动的情况。拟议的预测模型包括共享和适应参数,每个参数都针对用户的一般和个体运动。仅使用个别用户的少量数据,调整了适应参数,以便通过两步进程(通过单独的网络初始化和通过几个梯度步骤的适应)进行用户特定的预测。关于与机器人设备协作的20个用户的移动数据集,拟议方法在预测未来用户移动时,超越了现有元学习和非元学习基线。