克服人工神经网络灾难性遗忘的连续学习算法研究OA

Traditional artificial neural network training typically focuses on closed,static,independent and identically distributed data,and performs a single task after completing offline training.However,when the data distribution continuously changes with the environment,the model will forget the knowledge learned from previous tasks,a phenomenon known as"catastrophic forgetting".As an emerging learning paradigm,continual learning aims to endow models with the ability to continuously learn,accumulate,and consolidate knowledge from data streams with constantly changing distributions.This enables artificial neural networks to achieve a"stability-plasticity"balance,thereby overcoming catastrophic forgetting.Through in-depth analysis of the key characteristics of current continual learning algorithms,a real-world robotic physical verification platform was established.The effectiveness of continual learning algorithms was verified in the scenario of robotic physical object grasping.Experimental results show that when the Contrastive Correlation Preserving Replay(CCPR)algorithm is applied to the robotic physical object grasping task,the average accuracy of the grasping task increases by 26.67％,better assisting the robot in performing the target task.