ABSTRACT In this paper, we address sequential mobility assistance for daily elderly care through physical human–robot interaction. The goal of this work is to develop a robotic assistive system to provide physical support in daily life such as movement transition, e.g. sit-to-stand and walking. Using a mobile human support robotic platform, we propose an unsupervised learning-based approach to providing desirable physical support through an adaptive impedance parameter selection strategy according to the recognized user's movement state in an online manner. Using a latent generative model with a long short-term memory-based variational autoencoder, we first estimate the probability of the user's current movement state based on the sensory information in a low dimensional latent space. Then, the desired impedance parameters are selected adaptively according to the estimated movement state. One of the benefits of such an unsupervised learning approach is that no labeling is necessary in the training phase. Furthermore, our proposed framework is capable of detecting possible novel states such as falling over based on the obtained latent space. In order to demonstrate the proof of concept of our proposed approach, we present the experimental results of performance evaluations of online movement state recognition as well as novel movement detection. GRAPHICAL ABSTRACT

中文翻译：

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使用潜在的运动状态生成模型实现基于物理交互的顺序移动辅助

摘要在本文中，我们通过人机交互来解决日常老年人护理的顺序移动辅助问题。这项工作的目标是开发一个机器人辅助系统，以在日常生活中提供身体支持，例如运动过渡，例如从坐到站和步行。使用移动人类支持机器人平台，我们提出了一种基于无监督学习的方法，通过自适应阻抗参数选择策略以在线方式根据识别出的用户的运动状态提供理想的物理支持。使用具有基于长短期记忆的变分自编码器的潜在生成模型，我们首先根据低维潜在空间中的感官信息估计用户当前运动状态的概率。然后，根据估计的运动状态自适应地选择所需的阻抗参数。这种无监督学习方法的好处之一是在训练阶段不需要标记。此外，我们提出的框架能够根据获得的潜在空间检测可能的新状态，例如跌倒。为了证明我们提出的方法的概念证明，我们展示了在线运动状态识别以及新运动检测的性能评估的实验结果。图形概要 我们提出的框架能够根据获得的潜在空间检测可能的新状态，例如跌倒。为了证明我们提出的方法的概念证明，我们展示了在线运动状态识别以及新运动检测的性能评估的实验结果。图形概要 我们提出的框架能够根据获得的潜在空间检测可能的新状态，例如跌倒。为了证明我们提出的方法的概念证明，我们展示了在线运动状态识别以及新运动检测的性能评估的实验结果。图形概要