Visual Servoing for Floppy Robots Using LWPR

We have combined inverse kinematics learned by LWPR with visual servoing to correct for inaccuracies in a low cost robotic arm. By low cost we mean weak inaccurate servos and no available joint-feedback. We show that from the trained LWPR model the Jacobian can be estimated. The Jacobian maps wanted changes in position to corresponding changes in control signals. Estimating the Jacobian for the first iteration of visual servoing is straightforward and we propose an approximative updating scheme for the following iterations when the Jacobian can not be estimated exactly. This results in a sufficient accuracy to be used in a shape sorting puzzle.

A Developmental Organization for Robot Behavior

This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. In our model, the events that delineate control decisions are derived from the pattern of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential knowledge gathering and representation tasks and provide examples of the kind of developmental milestones that this approach has already produced in our lab

Generalized classical and quantum signal theories on hypergroups. Part 1. Clasical signal theory

In this paper we develop generalized nonharmonic analysis of signals and images on commu­tative hypergroups, associated with arbitrary unitary (orthogonal) transforms. We introduce general­ized convolutions, correlations, Wigner-Ville distributions, and ambiguity functions. All theorems and properties of ordinary classical Fourier harmonic analysis are transferred on nonharmonic analysis Fourier on arbitrary Abelian hypergroups

Will intelligent vehicles evolve into human-peer robots?

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Time-causal and time-recursive spatio-temporal receptive fields

We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, based on a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain. Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale. Specifically, extensions are presented about (i) parameterizing the intermediate temporal scale levels, (ii) analysing the resulting temporal dynamics, (iii) transferring the theory to a discrete implementation, (iv) computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision. We show that by distributing the intermediate temporal scale levels according to a logarithmic distribution, we obtain much faster temporal response properties (shorter temporal delays) compared to a uniform distribution. Specifically, these kernels converge very rapidly to a limit kernel possessing true self-similar scale-invariant properties over temporal scales, thereby allowing for true scale invariance over variations in the temporal scale, although the underlying temporal scale-space representation is based on a discretized temporal scale parameter. We show how scale-normalized temporal derivatives can be defined for these time-causal scale-space kernels and how the composed theory can be used for computing basic types of scale-normalized spatio-temporal derivative expressions in a computationally efficient manner.Comment: 39 pages, 12 figures, 5 tables in Journal of Mathematical Imaging and Vision, published online Dec 201

Correlation Index: Document Series and PB Reports

Shortly after World War II, the President created the Publication Board, an interagency committee, and authorized it to distribute government documents bottled up by war-time secrecy as well as documents containing information obtained from World War II enemies. The Publication Board began advertising documents for sale in the Bibliography of Scientific and Industrial Reports (BSIR) in 1946. Those documents were assigned PB numbers which were to be used as order numbers when ordering documents from the Publication Board. The documents distributed by the Publication Board soon came to be known as technical reports. The Publication Board eventually evolved into the National Technical Information Service (NTIS). BSIR evolved into the NTIS online database. This index correlates the report numbers assigned by the issuing agencies with the PB numbers assigned by the Publication Board and its successor agencies. This index is particularly useful because most of the reports issued before 1962 are effectively lost. They are not listed in the NTIS database, and -- as of January 1, 2012 -- most of the reports themselves are not available online and most are not listed in any online database. For further information see The “Lost” U.S. Technical Reports: Obtaining Reports from the 1940s and ‘50s by Robert L. Bolin which is available at: http://digitalcommons.unl.edu/libraryscience/15