Recently, data-driven models such as deep neural networks have shown to be promising tools for modelling and state inference in soft robots. However, voluminous amounts of data are necessary for deep models to perform effectively, which requires exhaustive and quality data collection, particularly of state labels. Consequently, obtaining labelled state data for soft robotic systems is challenged for various reasons, including difficulty in the sensorization of soft robots and the inconvenience of collecting data in unstructured environments. To address this challenge, in this paper, we propose a semi-supervised sequential variational Bayes (DSVB) framework for transfer learning and state inference in soft robots with missing state labels on certain robot configurations. Considering that soft robots may exhibit distinct dynamics under different robot configurations, a feature space transfer strategy is also incorporated to promote the adaptation of latent features across multiple configurations. Unlike existing transfer learning approaches, our proposed DSVB employs a recurrent neural network to model the nonlinear dynamics and temporal coherence in soft robot data. The proposed framework is validated on multiple setup configurations of a pneumatic-based soft robot finger. Experimental results on four transfer scenarios demonstrate that DSVB performs effective transfer learning and accurate state inference amidst missing state labels.

翻译：近期，深度神经网络等数据驱动模型已被证明是软体机器人建模与状态推断的有效工具。然而，深度模型的高效运行需要海量数据支持，这要求进行详尽且高质量的数据采集，尤其体现在状态标签的获取上。受制于软体机器人传感化困难、非结构化环境数据采集不便等因素，为软体机器人系统标注状态数据面临诸多挑战。针对这一问题，本文提出一种半监督顺序变分贝叶斯（DSVB）框架，用于解决特定构型软体机器人因缺失状态标签导致的迁移学习与状态推断难题。考虑到软体机器人在不同构型下可能呈现迥异的动力学特性，我们同步引入特征空间迁移策略，以增强多构型间潜在特征的适应性。与现有迁移学习方法不同，本研究所提DSVB框架采用循环神经网络建模软体机器人数据中的非线性动力学与时序连贯性。通过在气动软体手指的多组构型实验验证，四个迁移场景的测试结果表明，DSVB能在状态标签缺失条件下实现高效的迁移学习与精确的状态推断。