Natural living systems such as wood frogs develop tissues composed of active hydrogels with cryoprotectants to survive in cold environments. Recently, hydrogels have been intensively studied to develop stretchable electronics for wearables and soft robots. However, regular hydrogels are inevitably frozen at the subzero temperature and easily dehydrated, and have weak surface adhesion. Herein, a novel hydrogel-based ionic skin (iSkin) capable of strain sensing is demonstrated with high toughness, high stretchability, excellent ambient stability, superior anti-freezing capability, and strong surface adhesion. The iSkin consists of a piece of ionically and covalently cross-linked tough hydrogel with a thin bioadhesive layer. With the addition of biocompatible cryoprotectant and electrolyte, the iSkin shows good conductivity in wide ranges of relative humidity (15-90%) and temperature (−95-25 °C). In addition, the iSkin can adhere firmly to diverse material surfaces under different conditions, including cloth fabric, skin, and elastomers, in both dry and wet conditions, at subzero temperature, and/or with dynamic movement. The iSkin is demonstrated for applications including strain sensing on both human body and winter coat, human-machine interaction, motion/deformation sensing on a soft gripper and a soft robot at extremely cold conditions. This work provides a new paradigm for developing high-performance artificial skins for wearable sensing and soft robotics.

Concentric tube continuum robots utilize nested tubes, which are subject to a set of inequalities. Current approaches to account for inequalities rely on branching methods such as if-else statements. It can introduce discontinuities, may result in a complicated decision tree, has a high wall-clock time, and cannot be vectorized. This affects the behavior and result of downstream methods in control, learning, workspace estimation, and path planning, among others.In this paper, we investigate a mapping to mitigate branching methods. We derive a lower triangular transformation matrix to disentangle the inequalities and provide proof for the unique existence. It transforms the interdependent inequalities into independent box constraints. Further investigations are made for sampling, control, and workspace estimation. Approaches utilizing the proposed mapping are at least 14 times faster (up to 176 times faster), generate always valid joint configurations, are more interpretable, and are easier to extend.