Research Status and Prospects of Brain-inspired Situational Cognition Methods for Intelligent Robots
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摘要: 机器人的最高境界是拥有类脑智能并像人和动物一样具有智能行为. 随着对机器人感知、认知能力要求的不断提高, 传统人工智能方法逐渐陷入瓶颈. 自然界中的哺乳动物拥有卓越的情景认知能力, 借鉴其大脑的神经信息传递和处理机制, 研究机器人类脑情景认知方法已成为研究热点. 首先介绍大鼠、猕猴等哺乳动物的情景认知神经机理, 进而探讨受其启发的情景认知计算模型, 随后概述机器人类脑情景认知方法研究情况, 最后总结当前研究面临的挑战并展望未来发展方向.Abstract: The highest level of robotics is to achieve brain-inspired intelligence and exhibit intelligent behaviors comparable to those of humans and animals. As demands for robots$'$ perception and cognitive capabilities continue to rise, traditional artificial intelligence approaches are encountering significant bottlenecks. Mammals in nature exhibit remarkable situational cognition abilities. Inspired by the neural information transmission and processing mechanisms of their brains, research on brain-inspired situational cognition methods for robots has emerged as a prominent topic. This paper first introduces the situational cognition mechanisms of mammals such as rats and macaques, then explores situational cognition computational models inspired by these mechanisms, and reviews current research on brain-inspired situational cognition methods for intelligent robots. Finally, it summarizes the existing research challenges and envisions future development directions.
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图 2 视觉通路信息传递示意图
Fig. 2 Schematic diagram of information transmission in the visual pathway
图 3 内嗅—海马通路信息传递示意图
Fig. 3 Schematic diagram of information transmission in the entorhinal–hippocampal pathway
图 5 视觉皮层与海马结构之间的信息传递通路
Fig. 5 Information transmission pathway between the visual cortex and hippocampal formation
图 6 时空细胞类型及其信息传递通路示意图
Fig. 6 Schematic diagram of spatiotemporal cell types and their information transmission pathways
图 10 基于LSTM的空间细胞路径积分模型及其实验结果
Fig. 10 Path integration model of spatial cells\based on LSTM and its experimental results
图 11 三代人工神经网络与生物神经系统的对比
Fig. 11 Comparison of three generations of artificial neural networks with biological nervous systems
图 13 基于内嗅—海马运行机理的认知地图构建系统
Fig. 13 Cognitive map construction system based on the operational mechanism of entorhina–hippocampal circuit
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