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A Non-linear Approach to Space Dimension Perception by a Naive Agent
A new approach is to consider perception as an experimentally acquired ability that is learned exclusively through the analysis of the agent's sensorimotor flow.
Learning agent's spatial configuration from sensorimotor invariants
We show that the notion of space as environment-independent cannot be deduced solely from exteroceptive information, which is highly variable and is mainly determined by the contents of the environment.
Learning an internal representation of the end-effector configuration space
Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or end-effector position.
Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience
We here show that the structure of space can be autonomously discovered by a naive agent in the form of sensorimotor regularities, that correspond to so called compensable sensory experiences: these are experiences that can be generated either by the agent or its environment.
