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，深度模型需要海量数据才能发挥良好性能，这要求进行详尽且高质量的数据采集，特别是状态标签数据。由于软体机器人传感系统部署困难、非结构化环境数据采集不便等多重原因，获取软体机器人系统的标注状态数据面临诸多挑战。为解决此问题，本文提出一种半监督序列变分贝叶斯（DSVB）框架，用于在部分机器人构型缺失状态标签的情况下实现软体机器人的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显著差异的动态特性，本框架引入特征空间迁移策略以促进潜在特征在多种构型间的自适应调整。与现有迁移学习方法不同，所提出的DSVB框架采用循环神经网络对软体机器人数据中的非线性动态特性与时序相关性进行建模。该框架在气动软体机器人手指的多种设置构型上得到验证。在四种迁移场景下的实验结果表明，DSVB能够在缺失状态标签的情况下实现有效的迁移学习与精确的状态推断。相关数据与代码已发布于 https://github.com/shageenderan/DSVB。