论文标题
语言指导机器动作
Language guided machine action
论文作者
论文摘要
在这里，我们构建了一个层次模块化网络，称为语言指导机器动作（LGMA），其模块流程信息流模仿人类皮质网络，该信息允许实现多个一般任务，例如语言指导动作，意图分解和动作执行之前的心理模拟等。LGMA包含3个主要系统：（1）具有视觉信息和传感器的多态感官信息的主要感觉系统。 （2）关联系统涉及和Broca模块，以理解和合成语言，BA14/40模块在感觉运动和语言之间转换，中间量的模块，以在语言和视觉之间转换，以及上层顶叶，以整合所经过的视觉对象和手臂状态为认知映射，以供未来的空间动作。支撑前运动区域（PER-SMA）可以将高水平的意图转换为顺序的原子作用，而SMA可以将这些原子动作，电流臂并将对象状态整合到感觉运动矢量中，以通过ARM的前电动机和ARM的主电动机在ARM上应用相应的扭矩，以实现意图。高级执行系统包含在基于语言的自愿行动的明确推理的PFC，而BG是惯常行动控制中心。
Here we build a hierarchical modular network called Language guided machine action (LGMA), whose modules process information stream mimicking human cortical network that allows to achieve multiple general tasks such as language guided action, intention decomposition and mental simulation before action execution etc. LGMA contains 3 main systems: (1) primary sensory system that multimodal sensory information of vision, language and sensorimotor. (2) association system involves and Broca modules to comprehend and synthesize language, BA14/40 module to translate between sensorimotor and language, midTemporal module to convert between language and vision, and superior parietal lobe to integrate attended visual object and arm state into cognitive map for future spatial actions. Pre-supplementary motor area (pre-SMA) can converts high level intention into sequential atomic actions, while SMA can integrate these atomic actions, current arm and attended object state into sensorimotor vector to apply corresponding torques on arm via pre-motor and primary motor of arm to achieve the intention. The high-level executive system contains PFC that does explicit inference and guide voluntary action based on language, while BG is the habitual action control center.
