Bio-inspired artificial intelligence robots: current challenges and potential research directions

Intelligent soft robotics is a relatively new yet satisfying technology that combines the advantages of major advances in embodied intelligence with the natural softness of robotic structures. The field of soft robotics aspires to equip robots with novel, bio-inspired qualities that allow them to structurally adjust to various environments. This study takes a mechanical approach to bioinspired soft robots by discussing the mechanics behind material selection, actuation, and design. Soft materials are necessary for biologically inspired robots' safe interaction and general functioning. Soft robots, which can learn via experience, have the potential to reduce mechanical and computational complexity. Creating completely autonomous, self-learning agents is now feasible by combining deep reinforcement learning (DRL) algorithms with a soft, bio-inspired architecture. Developing the mathematical models needed to train soft robotics utilizing deep reinforcement learning (SR-DRL) agents in soft robotic systems with numerous degrees of freedom. Using cutting-edge imitation learning algorithms in soft robotic systems has shown encouraging results. This article overviews many such algorithms and illustrates their use using examples from actual applications. The experimental outcomes show that the suggested SR-DRL system achieves a high accuracy ratio of 96% and a reliability ratio of 89% compared to other existing models.