Based on indications from neuroscience and psychology, both perception and action can be internally simulated in or- ganisms by activating sensory and/or motor areas in the brain without actual external sensory input ...Based on indications from neuroscience and psychology, both perception and action can be internally simulated in or- ganisms by activating sensory and/or motor areas in the brain without actual external sensory input and/or without any resulting behavior (a phenomenon called Thinking). This phenomenon is usually used by the organisms to cope with missing external inputs. Applying such phenomenon in a real robot recently has taken the attention of many researchers. Although some work has been reported on this issue, none of this work has so far considered the potential of the robot’s vision at the sensorimotor abstraction level, where extracting data from the environment takes place. In this study, a novel visiomotor abstraction is presented into a physical robot through a memory-based learning algorithm. Experi- mental results indicate that our robot with its vision could develop a kind of simple anticipation mechanism into its tree-type memory structure through interacting with the environment which would guide its behavior in the absence of external inputs.展开更多
文摘Based on indications from neuroscience and psychology, both perception and action can be internally simulated in or- ganisms by activating sensory and/or motor areas in the brain without actual external sensory input and/or without any resulting behavior (a phenomenon called Thinking). This phenomenon is usually used by the organisms to cope with missing external inputs. Applying such phenomenon in a real robot recently has taken the attention of many researchers. Although some work has been reported on this issue, none of this work has so far considered the potential of the robot’s vision at the sensorimotor abstraction level, where extracting data from the environment takes place. In this study, a novel visiomotor abstraction is presented into a physical robot through a memory-based learning algorithm. Experi- mental results indicate that our robot with its vision could develop a kind of simple anticipation mechanism into its tree-type memory structure through interacting with the environment which would guide its behavior in the absence of external inputs.
