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Pneumatic soft robots present many advantages in manipulation tasks. Notably,their inherent compliance makes them safe and reliable in unstructured andfragile environments. However, full-body shape sensing for pneumatic softrobots is challenging because of their high degrees of freedom and complexdeformation behaviors. Vision-based proprioception sensing methods relying onembedded cameras and deep learning provide a good solution to proprioceptionsensing by extracting the full-body shape information from the high-dimensionalsensing data. But the current training data collection process makes itdifficult for many applications. To address this challenge, we propose anddemonstrate a robust sim-to-real pipeline that allows the collection of thesoft robot's shape information in high-fidelity point cloud representation. Themodel trained on simulated data was evaluated with real internal camera images.The results show that the model performed with averaged Chamfer distance of8.85 mm and tip position error of 10.12 mm even with external perturbation fora pneumatic soft robot with a length of 100.0 mm. We also demonstrated thesim-to-real pipeline's potential for exploring different configurations ofvisual patterns to improve vision-based reconstruction results. The code anddataset are available at https://github.com/DeepSoRo/DeepSoRoSim2Real.

Toward Zero-Shot Sim-to-Real Transfer Learning for Pneumatic Soft Robot 3D Proprioceptive Sensing