Abstract
This paper presents a novel scheme for sensory-based navigation of a mobile robot: the robot is trained to learn a goal-directed task under adequate supervision, utilizing local sensory inputs. Focusing on the topological changes of temporal sensory flow, our scheme constructs a correct mapping from sensory input sequences to the maneuvering outputs through neural adaptation such that a hypothetical vector field that achieves the goal can be generated. The simulation experiments show that a robot, utilizing our scheme, can learn tasks of homing and sequential routing successfully in the work space of a certain geometrical complexity.
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