  Soft robotics is a modern robotic paradigm for performing dexterous
interactions with the surroundings via morphological flexibility. The desire
for autonomous operation requires soft robots to be capable of proprioception
and makes it necessary to devise a calibration process. These requirements can
be greatly benefited by adopting numerical simulation for computational
efficiency. However, the gap between the simulated and real domains limits the
accurate, generalized application of the approach. Herein, we propose an
unsupervised domain adaptation framework as a data-efficient, generalized
alignment of these heterogeneous sensor domains. A dual cross-modal autoencoder
was designed to match the sensor domains at a feature level without any
extensive labeling process, facilitating the computationally efficient
transferability to various tasks. As a proof-of-concept, the methodology was
adopted to the famous soft robot design, a multigait soft robot, and two
fundamental perception tasks for autonomous robot operation, involving
high-fidelity shape estimation and collision detection. The resulting
perception demonstrates the digital-twinned calibration process in both the
simulated and real domains. The proposed design outperforms the existing
prevalent benchmarks for both perception tasks. This unsupervised framework
envisions a new approach to imparting embodied intelligence to soft robotic
systems via blending simulation.
