Enhancing Robot Arm Control with an Improved Deep Deterministic Policy Gradient Algorithm

Published on January 23, 2023

Imagine trying to control a robot arm. It can be a real challenge! But fear not, scientists are working on making it easier. In this study, researchers aimed to improve the control of robotic arms by enhancing the deep deterministic policy gradient (DDPG) algorithm. They designed a hybrid function that combines different rewards to make the algorithm more robust and adaptable to different situations. They also made improvements to the experience replay mechanism of DDPG by using priority sampling and uniform sampling, which helped the algorithm converge faster. The researchers tested their improved DDPG algorithm in a simulation environment and found that it achieved accurate control of the robot arm motion. In fact, the average success rate in the end-reaching task was an impressive 91.27%! This improved algorithm also showed better environmental adaptability compared to the original DDPG algorithm. To learn more about how they did it, check out the research article!

Aiming at the poor robustness and adaptability of traditional control methods for different situations, the deep deterministic policy gradient (DDPG) algorithm is improved by designing a hybrid function that includes different rewards superimposed on each other. In addition, the experience replay mechanism of DDPG is also improved by combining priority sampling and uniform sampling to accelerate the DDPG’s convergence. Finally, it is verified in the simulation environment that the improved DDPG algorithm can achieve accurate control of the robot arm motion. The experimental results show that the improved DDPG algorithm can converge in a shorter time, and the average success rate in the robotic arm end-reaching task is as high as 91.27%. Compared with the original DDPG algorithm, it has more robust environmental adaptability.

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