Unsupervised Intelligent Pose Estimation of Origami-Inspired Deployable Robots

Origami is the art of folding paper into different shapes and structures, implying wide interdisciplinary usage. Vision-based structural tracking can help determine the suitable control and intelligent actions required based on the pose of the origami robot. This chapter deals with various unsupervised learning for multi-DOF pose estimation using a single camera as an attempt to do general-purpose origami tracking. We first discuss planar 2D tracking the position of features in a sequence of planar frames and motion analysis of origami structures. Then we deal with unsupervised learning methods for 6-DOF pose estimation using simulation data. We introduce deep learning frameworks to perform semantic segmentation and pose prediction for origami structures by analyzing images from both real-world and simulations.