Robust Phototaxis by Harnessing Implicit Communication in Modular Soft Robotic Systems

In robotics, achieving adaptivity in complex environments is challenging. Traditional robotic systems use stiff materials and computationally expensive centralized controllers, while nature often favors soft materials and embodied intelligence. Inspired by nature’s distributed intelligence, this study explores a decentralized approach for robust behavior in soft robotic systems without knowledge of their shape or environment. It is demonstrated that only a few basic rules implemented in identical modules that shape the soft robotic system can enable whole-body phototaxis, navigating on a surface toward a light source, without explicit communication between modules or prior system knowledge. The results reveal the method’s effectiveness in generating robust and adaptive behavior in dynamic and challenging environments. Moreover, the approach’s simplicity makes it possible to illustrate and understand the underlying mechanism of the observed behavior, paying particular attention to the geometry of the assembled system and the effect of learning parameters. Consequently, the findings offer insights into the development of adaptive, autonomous robotic systems with minimal computational power, paving the way for robust and useful behavior in soft and microscale robots, as well as robotic matter, that operate in real-world environments.