A Needs Learning Algorithm Applied to Stable Gait Generation of Quadruped Robot.

Based on Maslow's hierarchy of needs theory, we have proposed a novel machine learning algorithm that combines factors of the environment and its own needs to make decisions for different states of an agent. This means it can be applied to the gait generation of a quadruped robot, which needs to make demand decisions. To evaluate the design, we created an experimental task in order to compare the needs learning algorithm with a reinforcement learning algorithm, which was also derived from psychological motivation theory. It was found that the needs learning algorithm outperformed the reinforcement learning in tasks that involved making decisions between different levels of needs. Finally, we applied the needs learning algorithm to the problem of stable gait generation of quadruped robot, and it had achieved good results in simulation and real robot.