用深度学习模拟类意识训练，让机器人学会复杂任务推理与泛化
Deep Learning based Quasi-consciousness Training for Robot Intelligent Model
- 构建环境因子矩阵驱动模型学习，通过粗调与细调优化损失函数
- 机器人需1~3年特殊训练才能具备基础意识，实现复杂信息处理与决策
- 适用于需要自主认知与泛化能力的智能机器人研发
本文探讨基于深度学习的机器人智能模型，使其能够学习并推理复杂任务。首先，通过构建环境因子矩阵激发机器人智能模型的学习过程，模型参数需经过粗调与细调以优化损失函数，最小化损失得分；同时，该模型可融合已有概念，表征未曾经历的事物，具备广泛泛化能力。其次，为逐步发展具备初级意识的机器人智能模型，每个机器人必须接受至少1至3年的特殊学校式训练，以习得类人行为模式，理解并处理复杂环境信息，做出理性决策。本研究探索并展示了基于深度学习的类意识训练在机器人智能模型领域的潜在应用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。