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. The data and code are available at https://github.com/shageenderan/DSVB.

翻译：近年来，深度神经网络等数据驱动模型在软体机器人建模与状态推断方面展现出巨大潜力。然而，深度模型的有效运行需要海量数据支撑，这要求进行详尽且高质量的数据采集，尤其是状态标签的获取。由于软体机器人传感器部署困难以及在非结构化环境中数据采集不便等多种原因，获取带有标签的状态数据面临挑战。针对这一问题，本文提出一种半监督序贯变分贝叶斯（DSVB）框架，用于在特定机器人构型状态标签缺失的情况下实现迁移学习与状态推断。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，我们进一步引入特征空间迁移策略，以促进多构型间潜在特征的适应性调整。与现有迁移学习方法不同，本文提出的DSVB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特性与时序连贯性。该框架在气动软体机器人手指的多种构型设置中得到验证。四个迁移场景的实验结果表明，DSVB能在标签缺失条件下实现有效的迁移学习与精准的状态推断。相关数据与代码已开源至https://github.com/shageenderan/DSVB。