Robotic arm grasping study combining prior knowledge and deep reinforcement learning

Abstract

In the process of applying deep reinforcement learning (DRL) to realize autonomous behavioral decision-making of robotic arms, the high-dimensional continuous state-action space is prone to low data sampling efficiency and low quality of empirical samples, which ultimately leads to slow convergence of the reward function and long learning time. To address this problem, a DRL model that introduces prior knowledge was proposed. The model was combined with the inverse kinematics of the robotic arm, and prior knowledge was introduced to guide the agent during the sampling phase of DRL, addressing the issues of low data sampling efficiency and poor quality of experience samples during the learning process. Furthermore, the introduced prior knowledge DRL model's strong generalization capabilities were verified when facing new tasks through network parameter transfer. Lastly, joint simulation experiments were conducted using Python and the CoppeliaSim platform. The results show that the DRL model with the introduction of prior knowledge improves the learning efficiency by 13.89% and 12.82%, and the success rate of completing the task increases by 16.92% and 13.25% than the original model; in the new task, the learning rate improves by 23.08% and 23.33%, and the success rate improves by 10.7% and 11.57%.

Keywords

• robotic arms
• prior knowledge
• deep reinforcement learning
• network migration