Search

Continual learning, as a special machine learning paradigm that continuously learns new tasks in non-stationary data streams and can maintain the performance of old tasks, is a hot research topic in fields such as visual computing and autonomous robotics, but at this stage, the catastrophic forgetting problem is still a great challenge for continuous learning. This paper conducted a review study on the catastrophic forgetting problem of continual learning, analyzed the mechanism of catastrophic forgetting problem mitigation and explored the catastrophic forgetting problem solving strategies at three levels, included regularization strategy, replay strategy, dynamic architecture strategy and joint strategy, in terms of model parameters, training data and network architecture. According to the existing literature, this paper condensed the evaluation index of the catastrophic forgetting method and compared the performance of solving strategies for different catastrophic forgetting problems. Finally, it pointed out the future research direction of continual learning, to provide references for the study of continuous learning catastrophic forgetting problems. [ABSTRACT FROM AUTHOR]

This paper proposed an uncalibrated 3 D robotic arm grabbing method based on improved YOL0v2 in a multi-object environment. Firstly, in order to reduce the depth learning algorithm YOL0v2 detection multi-obj ect bounding box overlapping rate and 3D distance calculation error, it proposed an improved algorithm for YOL0v2. It used this algorithm to detect and identify the target object in the image, obtained the pos ition information of the target object in the RGB image, and then used the K-means + + clustering algorithm to quickly calculate the distance from the target object to the camera according to the depth image information, and estimate the target object size and pose. Simultaneously, it used the improved YOL0v2 to get the bounding box of the gripper and calculated the distance from the robot to the target object. Then the system estimated the distance between the fixture, camera and obj ect in the manipulator coordinate system. Finally, the system used the PID algorithm to control the gripper to grab the object according to the size and posture of the object and the distance from the object to the gripper. The detected boundary boxes of the target obj ect was more accurate based on the improved YOL0v2 than on old one. It also enhanced the distance from the fixture to the object and the size of the object as well as the accuracy of the pose estimation. In addition, in order to avoid complicated calibration, this paper proposed a non-calibration method. The learning scheme was different from the traditional uncalibrated estimation method based on Jacobian matrix, because it had good universality. A simulation experiment shows that the proposed method can accurately classify and locate the objects in the image. The Universal Robot 3 robotic arm uses this framework to verify the effectiveness of capturing objects in a cluttered environment. [ABSTRACT FROM AUTHOR]

A multi-agent reinforcement learning method based on action prediction of other agents is proposed. In the multi-agent system, the action selection of a learning agent must be effected by other agents' action. Therefore, joint-state and joint-action are involved in reinforcement learning system. The method of agent action prediction based on the probabilistic neural network is proposed. So the joint-action is formed and multi-agent reinforcement learning is implemented. Furthermore, the application of the proposed method in cooperation strategy learning of robot soccer is studied. Through mutually learning with environment, multiple robot system can master behavior strategy and realize multiple robots coordination and cooperation. [ABSTRACT FROM AUTHOR]