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Pneumatic soft robots have several advantages, including facile fabrication, versatile deformation modes, and safe human–machine interaction. However, pneumatic soft robots typically rely on mechatronics to interact with their environment, which can limit their form factors and reliability. Researchers have considered how to achieve autonomous behaviors using the principles of mechanical computing and physical intelligence. Herein, modular responsive valves that can autonomously regulate airflow within pneumatic soft robots in response to various environmental stimuli, including light, water, and mechanical forces, are described. By combining multiple types of valves, autonomous logic gates and more advanced logical operations can be realized. Finally, it is demonstrated that responsive valves can be integrated with pneumatic soft robots, allowing autonomous morphing and navigation. This framework provides a strategy for creating autonomous pneumatic robots that can respond to multiple stimuli in their environment. 

Robots typically interact with their environments via feedback loops consisting of electronic sensors, microcontrollers, and actuators, which can be bulky and complex. Researchers have sought new strategies for achieving autonomous sensing and control in next-generation soft robots. We describe here an electronics-free approach for autonomous control of soft robots, whose compositional and structural features embody the sensing, control, and actuation feedback loop of their soft bodies. Specifically, we design multiple modular control units that are regulated by responsive materials such as liquid crystal elastomers. These modules enable the robot to sense and respond to different external stimuli (light, heat, and solvents), causing autonomous changes to the robot’s trajectory. By combining multiple types of control modules, complex responses can be achieved, such as logical evaluations that require multiple events to occur in the environment before an action is performed. This framework for embodied control offers a new strategy toward autonomous soft robots that operate in uncertain or dynamic environments.