A Survey on Robot Arm Movement Optimization With Q-Learning

In Optimizing robotic arms’ motion and control is essential for improving productivity, accuracy, and versatility in a variety of applications. In order to enable robotic arms to learn optimal movements on their own rather than by following predetermined, fixed paths, this study investigates the use of Q-learning, a model-free reinforcement learning algorithm. The robotic arm is viewed as an agent that interacts with its surroundings; it is penalized for ineffective or collision-prone movements and rewarded for actions that bring it closer to a target. The Q-learning algorithm finds actions that maximize accuracy and minimize energy consumption over time by updating a Q-table. In dynamic and uncertain environments, this adaptive learning approach facilitates self-learning, intelligent decision-making, and real-time adaptability.