Reinforcement Learning Based Controller for a Soft Continuum Robot

Abstract

The application of soft robotics holds immense potential for transformative impact across diverse domains, encompassing healthcare, such as addressing cardiac obstructions, and exploration, like acquiring invaluable insights through polar ice cap navigation. Soft robots exhibit superior adaptability owing to their softness and flexibility. However, controlling soft robots poses challenges due to the absence of joints and rigidity. Consequently, a control strategy employing reinforcement learning is introduced for controlling their navigation. This strategy enables the soft robot to acquire knowledge from its environment through feedback in the form of rewards or penalties, resulting in exceptional precision in reaching its intended destination.