International Journal of Mechanical Engineering

Teaching programming for robotic arms is widely used in industrial automation, but traditional methods suffer from issues such as poor flexibility and low efficiency. To achieve more efficient and intelligent teaching programming, reinforcement learning technology has been introduced. By constructing a reinforcement learning model that includes the state space, action space, and reward function, the teaching programming process for robotic arms can be optimized adaptively. The model uses the joint angles of the robotic arm and the end effector's position as state inputs, and the joint motion commands as action outputs. A reward mechanism is established based on the task completion and energy consumption. Simulation and experimental results show that this optimization method significantly enhances the efficiency of teaching programming for robotic arms, reduces operational complexity, and provides effective technical support for the application of robotic arms in complex task scenarios.

Reinforcement learning; Robotic arm; Teaching programming; Adaptive optimization; Industrial automation