Deep Learning based Quasi-consciousness Training for Robot Intelligent Model
This work addresses the problem of creating more autonomous and conscious robots for applications in robotics, though it appears incremental as it builds on existing deep learning methods without clear benchmarks.
The paper tackles the challenge of developing a robot intelligent model capable of learning and reasoning for complex tasks, proposing a deep learning-based quasi-consciousness training approach that involves environmental factor matrices and extensive training periods of 1-3 years to achieve anthropomorphic behavior and generalization.
This paper explores a deep learning based robot intelligent model that renders robots learn and reason for complex tasks. First, by constructing a network of environmental factor matrix to stimulate the learning process of the robot intelligent model, the model parameters must be subjected to coarse & fine tuning to optimize the loss function for minimizing the loss score, meanwhile robot intelligent model can fuse all previously known concepts together to represent things never experienced before, which need robot intelligent model can be generalized extensively. Secondly, in order to progressively develop a robot intelligent model with primary consciousness, every robot must be subjected to at least 1~3 years of special school for training anthropomorphic behaviour patterns to understand and process complex environmental information and make rational decisions. This work explores and delivers the potential application of deep learning-based quasi-consciousness training in the field of robot intelligent model.