Recently, there has been a notable shift towards electronics-free designs, which offer promising integration possibilities with soft robots, reducing reliance on traditional electronics. Despite numerous demonstrations showcasing logical control, contact sensors, and gait control, the conventional quake valve-based design is gradually struggling to meet the demands of electronics-free soft robots with increasingly complex functionalities. Integrating multiple tubes, channels, and valves has led to larger and bulkier overall systems. In this study, we introduce a simple yet powerful electronics-free pneumatic valve that excels in various aspects: it allows for flexible function configurations (operating individually, in pairs, or in larger groups), offers high-frequency synchronous reverse outputs, stores valve status, and ensures efficient maintenance. We believe that this work lays the groundwork for developing straightforward yet highly effective fully autonomous soft robots.

Safe and efficient robot-environment interaction is a critical but challenging problem as robots are being increasingly employed to operate in unstructured and unpredictable environments. Soft robots are inherently compliant to safely interact with environments but their high nonlinearity exacerbates control difficulties. Meta-learning provides a powerful tool for fast online model adaptation because it can learn an efficient model from data across different environments. Thus, this work applies the idea of meta-learning for the control of soft robotics. In particular, a target-oriented proactive search strategy is firstly performed to collect environment-specific data efficiently when a new interaction environment occurs. Then meta-learning exploits past experience to train a data-driven probabilistic model prior, and the model prior is online updated to be fast adapted to the new environment. Lastly, a model-based optimal control policy is utilized to drive the robot to desired performance. Our approach controls a soft robotic manipulator to achieve the desired position and contact force simultaneously when interacting with unknown changing environments. Overall, this work provides a viable control approach for soft robots to interact with unknown environments.