Recent progress in black phosphorus-based photonics, electronics, sensors and energy devices has been reviewed.

Over the past decade, soft robot research has expanded to diverse fields, including biomedicine, bionics, service robots, human–robot interaction, and artificial intelligence. Much work has been done in modeling the kinematics and dynamics of soft robots, but closed‐loop control is still in its early stages due to limited sensory feedback. Thanks to the advancement in functional materials, structures, and manufacturing techniques for flexible electronics, flexible and stretchable sensors are developing rapidly. These sensors provide feedback for closed‐loop control tasks and enable soft robots to effectively explore the unknown and safely interact with humans and the environment. Herein, recent advances in perceptive soft robots that utilize flexible/stretchable sensors and functional materials are outlined. The perceptive functions of soft robots from two different aspects, that is, proprioception and exteroception, are summarized. Furthermore, the constructions of autonomous soft robots by integrating both proprioceptive and exteroceptive capabilities for closed‐loop control tasks and other challenging tasks in the real world are discussed.

The past decades have seen the rapid development of tactile sensors in material, fabrication, and mechanical structure design. The advancement of tactile sensors has heightened the expectation of sensor functions, and thus put forward a higher demand for data processing. However, conventional analysis techniques have not kept pace with the tactile sensor development and still suffer from some severe drawbacks, like cumbersome models, poor efficiency, and expensive costs. Machine learning, with its prominent ability for big data analysis and fast processing speed, can offer many possibilities for tactile data analysis. Herein, the machine learning techniques employed for processing tactile signals are reviewed. Supervised learning and unsupervised learning for analog signals are covered, and processing spike signals with machine learning are summarized. Furthermore, the applications in robotic tactile perception and human activity monitoring are presented. Finally, the current challenges and future prospects in sensors, data, algorithms, and benchmarks are discussed.