Artem Guba

This paper describes a comprehensive approach to the development of a quadrupedal robot possessing 12 actuated degrees of freedom. The development comprises the design of the mechanical system, the creation of control electronics, and the implementation of software for motion generation. A key aspect involves the application of reinforcement learning in a physical simulator, followed by the transfer of the trained algorithms to the physical device (sim-to-real). An embedded Neural Processing Unit (NPU) is utilized to accelerate the execution of AI algorithms, such as object recognition, navigation, and motion optimization. The proposed solutions enable efficient and symmetrical locomotion, high adaptability to changing environmental conditions, and enhanced operational autonomy of the robot.