  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.
