  Metamaterial mechanisms are micro-architectured compliant structures that
operate through the elastic deformation of specially designed flexible members.
This study develops an efficient design methodology for compliant mechanisms
using deep reinforcement learning (RL). For this purpose, design domains are
digitized into finite cells with various hinge connections, and finite element
analyses (FEAs) are conducted to evaluate the deformation behaviors of the
compliance mechanism with different cell combinations. The FEA data are learned
through the RL method to obtain optimal compliant mechanisms for desired
functional requirements. The RL algorithm is applied to the design of a
compliant door-latch mechanism, exploring the effect of human guidance and
tiling direction. The optimal result is achieved with minimal human guidance
and inward tiling, resulting in a threefold increase in the predefined reward
compared to human-designed mechanisms. The proposed approach is extended to the
design of a soft gripper mechanism, where the effect of hinge connections is
additionally considered. The optimal design under hinge penalization reveals
remarkably enhanced compliance, and its performance is validated by
experimental tests using an additively manufactured gripper. These findings
demonstrate that RL-optimized designs outperform those developed with human
insight, providing an efficient design methodology for cell-based compliant
mechanisms in practical applications.
