The Cognitive System of Robots Based on Deep Learning with Stable Convergence

With the advance of deep learning, improving the understanding and cognition of artificial intelligence (AI) systems has become an increasingly crucial research trend. Although most AI studies have focused on improving the efficiency and reach of deep learning technologies for the next wave of nascent AI solutions, they have also highlighted the real-world challenges and limitations of current deep learning approaches. In view of this, this paper proposes a novel cognitive system based on deep learning. To mathematically analyze and solve the critical problem of unstable convergence existing in general cognitive systems, we propose a system framework consisting of three models: a perception model, a hypothesis model, and a memory model. In contrast to conventional reinforcement learning systems, the online learning of our proposed cognitive system can be carried out by only comparing the current outputs with the expected inputs. Then, the memory model (as an evaluation model) can estimate the learning results more accurately so that the hypothesis model is capable of generating improved hypotheses. The contribution of our method is to refer to the memory theory in cognitive psychology to improve the stability of the image-to-robot motor end-to-end learning system. Moreover, an auto-encoder, as the perception model, can encode an observed image into a perception code as the features to easily find an optimal solution. To validate the effectiveness of the proposed cognitive system, Chinese calligraphy writing tasks are used to evaluate its performance. Experimental results show that the proposed cognitive system significantly enhances the online learning process with stable convergence and improves the writing performance of the calligraphy work.