Soft robots are developed and studied for their safety and adaptability in various applications. Compared to their rigid counterparts, soft robots can use their deformable bodies to adapt to challenging environments and tolerate collisions and inaccuracies. Natural animals, due to their intrinsic softness, have become popular bio-inspirations for many soft robots, which, in turn, could provide insights into biomechanics.Snakes, known for their adaptability and flexibility, inspire the development of limbless mobile robots for tasks in complex environments. Inspired by snakes, we propose a novel pneumatic soft snake robot that uses traveling-wave deformation to navigate complex, constrained environments such as pipeline systems. The unique pneumatic system in the modular snake robot generates traveling-wave deformation with only four independent air channels. Experimental results show good agreement with finite element method (FEM) predictions and demonstrate the robot's adaptability in complex pipeline systems. Additionally, a spiral-type soft snake robot is proposed for more robust locomotion in constrained environments, utilizing helix-like deformation for propulsion.Besides the locomotion in constrained environments, we develop a multi-material 3D-printed snakeskin with orthotropic frictional anisotropy, inspired by real snakeskin, to enable undulatory slithering of the robot on planar rough surfaces. This snakeskin comprises a soft base with embedded rigid scales, mimicking real snakeskin. The designs generate various frictional anisotropies that propel the robot during serpentine locomotion. Experiments show effective serpentine locomotion on artificial and outdoor surfaces like canvas and grass.Given the complexity of the dynamic model of the snake robot's serpentine locomotion, a model-free reinforcement learning approach is chosen for integrated locomotion and navigation. We propose Back-stepping Experience Replay (BER) to enhance learning efficiency in systems with approximate reversibility, reducing the need for complex reward shaping. BER is used in the soft snake robot's locomotion and navigation task, with a dynamic simulator assessing the algorithms' effectiveness. The robot achieves a 100\% success rate in learning, reaching random targets 48\% faster than the best baseline approach.In addition to mobile robots, bio-inspired soft robots have been proposed for robotic manipulators, enabling safe and robust interactions with humans and delicate objects. Inspired by octopus tentacles, we design a multi-section cable-driven soft robotic arm with novel kinematic modeling. An analytical static model captures the interaction between the actuation cable and the soft silicone body. Experiments show good agreement with model predictions and the soft robotic arm demonstrates high flexibility and a large workspace for potential human-machine interaction applications.
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