This paper presents an algorithm for camera calibration. The algorithm, inspired by work in the field of developmental robotic on the concept of space in naive agents, is particularly suitable for robotic applications: it is completely unsupervised, and it does not assume any model of the camera, making it applicable to many kinds of optical devices. Testing of the algorithm, in a simulated environment, shows very good results, outperforming the main unsupervised and model-free calibration algorithm in the literature.

Current machine learning techniques proposed to automatically discover a robot's kinematics usually rely on a priori information about the robot's structure, sensor properties or end-effector position. This paper proposes a method to estimate a certain aspect of the forward kinematics model with no such information. An internal representation of the end-effector configuration is generated from unstructured proprioceptive and exteroceptive data flow under very limited assumptions. A mapping from the proprioceptive space to this representational space can then be used to control the robot.

Is there a way for an algorithm linked to an unknown body to infer by itself information about this body and the world it is in? Taking the case of space for example, is there a way for this algorithm to realize that its body is in a three dimensional world? Is it possible for this algorithm to discover how to move in a straight line? And more basically: do these questions make any sense at all given that the algorithm only has access to the very high-dimensional data consisting of its sensory inputs and motor outputs? We demonstrate in this article how these questions can be given a positive answer. We show that it is possible to make an algorithm that, by ana- lyzing the law that links its motor outputs to its sensory inputs, discovers information about the structure of the world regardless of the devices constituting the body it is linked to. We present results from simulations demonstrating a way to issue motor orders resulting in “fundamental” movements of the body as regards the structure of the physical world.

This letter suggests that in biological organisms, the perceived structure of reality, in particular the notions of body, environment, space, object, and attribute, could be a consequence of an effort on the part of brains to account for the dependency between their inputs and their outputs in terms of a small number of parameters. To validate this idea, a procedure is demonstrated whereby the brain of a (simulated) organism with arbitrary input and output connectivity can deduce the dimensionality of the rigid group of the space underlying its input-output relationship, that is, the dimension of what the organism will call physical space.

The authors consider the problem of how humans can maintain a stable perception of object color across saccades in spite of the changes in sensory input caused by the spatially nonhomogeneous receptor spectral sensitivities. The authors propose a method, based on a temporal-difference reinforcement learning scheme, for constructing associations between pre- and post-motor stimuli and which yields a constant color perception across saccades.

Perceptually-tuned grayscale fonts are generated from character outline descriptions by applying to them a set of modifications specifically conceived for strengthening thin character parts, obtaining well-contrasted bars and preserving important relationships between character shape parts. The present study aims at comparing the legibility of perceptually-tuned grayscale and bilevel display fonts at small and very small sizes (6, 8 and 10 pt) The study confirms the results of previous studies indicating that reading speed is to a large extent independent of the typography (bilevel or grayscale) and the font size. However, perceptually-tuned grayscale characters perform better than bilevel characters for an italic string search task in a meaningless text. Regarding the subjective preferences of the test subjects, perceptually-tuned grayscale fonts at 8 and 10 point sizes received a superior rating than bilevel fonts at the same sizes.