Gaze Behavior, Believability, Likability and the iCat

The iCat is a user-interface robot with the ability to express a range of emotions through its facial features. This paper summarizes our research whether we can increase the believability and likability of the iCat for its human partners through the application of gaze behaviour. Gaze behaviour serves several functions during social interaction such as mediating conversation flow, communicating emotional information and avoiding distraction by restricting visual input. There are several types of eye and head movements that are necessary for realizing these functions. We designed and evaluated a gaze behaviour system for the iCat robot that implements realistic models of the major types of eye and head movements found in living beings: vergence, vestibulo ocular reflexive, smooth pursuit movements and gaze shifts. We discuss how these models are integrated into the software environment of the iCat and can be used to create complex interaction scenarios. We report about some user tests and draw conclusions for future evaluation scenarios

Visual task identification and characterisation using polynomial models

Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour based on theoretical models. Instead, current methods to develop robot control code still require a substantial trial-and-error component to the software design process. This paper proposes a method of dealing with these issues by a) establishing task-achieving sensor-motor couplings through robot training, and b) representing these couplings through transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour. We demonstrate the viability of this approach by teaching a mobile robot to track a moving football and subsequently modelling this task using the NARMAX system identification technique

BIM and forecasting deformations in monitoring structures

BIM technologies are becoming more widely used, mainly in the design and operation of buildings and structures, and in most cases this is enough for trouble-free operation. Nevertheless, there is a category of buildings for which the monitoring of the technical condition should be an integral part of the construction and operation. These are the so-called public large-span structures. Unfortunately, the development of BIM technology in the Russian Federation is not at such a level as to answer questions about the behaviour of objects under changing environmental conditions and reveal hidden patterns in the monitoring data. Based on the analysis of literary sources, the authors reviewed various methods for identifying hidden patterns in geodetic measurement data when monitoring buildings and structures. It is noted that modern analysis methods are based on statistical processing of measurement results and on the statistical method of forecasting. However, there are attempts to apply models that take into account the design features and the temperature regime of the object. This type includes the two proposed models, which are used to model the three-dimensional coordinates of the strain marks in the 3D model and only the elevations of the marks in the 1-Z model. The article presents the rationale for the simulated geometric elements and properties of the object. The solution of the equations of both models and the analysis of the results and parameters of the model for measurement epochs are shown. The simulation is shown on the example of a real object, which was monitored by the authors in 2015-2016. The authors believe that the monitoring of large-span structures and the search for patterns of their behaviour should be an integral part of the BIM system for such structures

A Method for Calculating the Structure of (Singular) Spacetimes in the Large

A formalism and its numerical implementation is presented which allows to calculate quantities determining the spacetime structure in the large directly. This is achieved by conformal techniques by which future null infinity (\Scri{}^+) and future timelike infinity ($i^+$) are mapped to grid points on the numerical grid. The determination of the causal structure of singularities, the localization of event horizons, the extraction of radiation, and the avoidance of unphysical reflections at the outer boundary of the grid, are demonstrated with calculations of spherically symmetric models with a scalar field as matter and radiation model.Comment: 29 pages, AGG2

Learning by observation through system identification

In our previous works, we present a new method to program mobile robots —“code identification by demonstration”— based on algorithmically transferring human behaviours to robot control code using transparent mathematical functions. Our approach has three stages: i) first extracting the trajectory of the desired behaviour by observing the human, ii) making the robot follow the human trajectory blindly to log the robot’s own perception perceived along that trajectory, and finally iii) linking the robot’s perception to the desired behaviour to obtain a generalised, sensor-based model. So far we used an external, camera based motion tracking system to log the trajectory of the human demonstrator during his initial demonstration of the desired motion. Because such tracking systems are complicated to set up and expensive, we propose an alternative method to obtain trajectory information, using the robot’s own sensor perception. In this method, we train a mathematical polynomial using the NARMAX system identification methodology which maps the position of the “red jacket” worn by the demonstrator in the image captured by the robot’s camera, to the relative position of the demonstrator in the real world according to the robot. We demonstrate the viability of this approach by teaching a Scitos G5 mobile robot to achieve door traversal behaviour

Exploring the concept of interaction computing through the discrete algebraic analysis of the Belousov–Zhabotinsky reaction

Interaction computing (IC) aims to map the properties of integrable low-dimensional non-linear dynamical systems to the discrete domain of finite-state automata in an attempt to reproduce in software the self-organizing and dynamically stable properties of sub-cellular biochemical systems. As the work reported in this paper is still at the early stages of theory development it focuses on the analysis of a particularly simple chemical oscillator, the Belousov-Zhabotinsky (BZ) reaction. After retracing the rationale for IC developed over the past several years from the physical, biological, mathematical, and computer science points of view, the paper presents an elementary discussion of the Krohn-Rhodes decomposition of finite-state automata, including the holonomy decomposition of a simple automaton, and of its interpretation as an abstract positional number system. The method is then applied to the analysis of the algebraic properties of discrete finite-state automata derived from a simplified Petri net model of the BZ reaction. In the simplest possible and symmetrical case the corresponding automaton is, not surprisingly, found to contain exclusively cyclic groups. In a second, asymmetrical case, the decomposition is much more complex and includes five different simple non-abelian groups whose potential relevance arises from their ability to encode functionally complete algebras. The possible computational relevance of these findings is discussed and possible conclusions are drawn