Acquisition of a peristaltic crawling robot's motion pattern using reinforcement learning

In disaster areas, rescue work by humans is extremely difficult. Therefore, rescue work using rescue robots in place of humans is attracting attention. This study specifically examines peristaltic crawling, the movement mechanism of an earthworm, because it can enable movement through narrow spaces and because it can provide stable movement according to various difficult environments. We develop a robot using peristalsis characteristics and derive a robot motion pattern using Q-learning, a mode of reinforcement learning. Additionally, we confirmed the convergence to the most suitable solution by coordinating Q-learning parameters.