Abstract
Automatic robot activity understanding plays an important role in human–computer interaction (HCI), especially in smart home service robots. Existing manipulator control methods, such as position control, vision-based control method, fail to meet the requirements of autonomous learning. Reinforcement learning can cope with the interaction of robot control and environment; however, the method should relearn the control method when the position of target object changes. To solve this problem, this paper proposes a quality model to utilize deep reinforcement learning scheme to achieve an end-to-end manipulator control. Specifically, we design a policy search algorithm to achieve automatic learning of manipulator. To avoid relearning of manipulator, we design convolutional neural network control scheme to remain the robustness of manipulator. Extensive experiment has shown the effectiveness of our proposed method.
•The existing manipulator control methods (such as position control, vision based control) can not meet the requirements of autonomous learning.•Therefore, this paper proposes a quality model, which uses the deep reinforcement learning scheme to realize the end-to-end manipulator control.•In the experiment, we design a convolution neural network control scheme to maintain the robustness of the manipulator, and the results show that the method is effective.