Volume 1, Issue 2

Motivation is a central concept in the development of autonomous agents and robots. This paper describes an architecture that uses a psychological BDI model of reasoning, combined with a distributed multi-level model of motivation. The robot controlling architecture makes use of a generic set of deliberative components plus an environment task-centred set of reactive components that reflect the architecture's embodiment. The architecture has been used in a number of simulated environments and here is used to control a mobile robot. A theoretical framework for motivation and affect is given, and related to the nature of autonomy and embodiment. A BDI model, based on a psychological model of reasoning in a 5 year old child, is described in terms of the nature of motivation and affect within the architecture. Finally, criteria for judging the nature of an agent's motivation are introduced, and used to validate the motivational constructs implemented within the architecture. Experimental results lead to a comparative discussion.

Medical robots are expected to help with providing care in an aging society. The degree to which patients experience embarrassment in a medical examination might be influenced by the robots’ level of anthropomorphism. The results of our preliminary study show that young, healthy, Dutch university students were less embarrassed when interacting with a technical box than with a robot. Highly human-like robots might therefore not be the best choice for a medical robot. This result also shows that the robot was perceived as a person more so than the technical box. The next step is to compare the robot to a real nurse or doctor. If patients are less embarrassed when interacting with a robot, then, potentially, patients will be less likely to defer important medical examinations when carried out by medical robots.

In recent years, the interest in human-robot interactions has added a new dimension to the on-line path planning problem by requiring a method that guarantees a risk-free path. This paper presents a streamlined search algorithm for fast path modification. The algorithm is formulated as an optimization problem that evaluates alternative paths nearby each obstacle. Each path is evaluated based on the value of the danger assigned to that path. To reduce the size of the search space, the minimum number of via points necessary to alter the path is initially obtained using a geometrical method. Given the number of via points, the algorithm proceeds to locate the via points around the obstacle such that the resulting path through these via points satisfies all problem constraints. Obtaining a solution in this way renders a fast algorithm for path modification, while it better avoids problems often encountered in other gradient-based search algorithms. Case studies for two planar robots are provided to highlight some of the advantages of the proposed algorithm. Experimental results using a CRS-F3 robot manipulator validate the effectiveness of the algorithm for applications involving human-robot interactions.

In the double-plane method for stereo vision system calibration, the correspondence between screen coordinates and location in 3D space is calculated based on four plane-to-plane transformations; there are two planes of the calibration pattern and two cameras. The method is intuitive, and easy to implement, but, the main disadvantage is ill-conditioning for some spatial locations. In this paper we propose a method which exploits the third plane which physically does not belong to the calibration pattern, but can be calculated from the set of reference points. Our algorithm uses a combination of three calibration planes, with weights which depend on screen coordinates of the point of interest; a pair of planes which could cause numerical errors receives small weights and have practically no influence on the final results. We analyse errors, and their distribution in 3D space, for the basic and the improved algorithm. Experiments demonstrate high accuracy and reliability of our method compared to the basic version; root mean square error and maximum error, are reduced by factors of 4 and 20 respectively.

We try to perform geometrization of psychology by representing mental states, “ideas,” by points of a metric space–mental space. Evolution of ideas is described by dynamical systems in metric mental space. We apply the mental space approach for modeling of flows of unconscious and conscious information in the human brain. In a series of models, Models 1-4, we consider cognitive systems with increasing complexity of psychological behavior determined by the structure of flows of ideas. Since our models are in fact models of the AI-type, one immediately recognizes that they can be used for creation of AI-systems, which we call psycho-robots, exhibiting important elements of the human psyche. Creation of such psycho-robots may be useful in the improvement of domestic robots. At the moment domestic robots are merely simple working devices (e.g. vacuum cleaners or lawn mowers). However, in future one can expect demand for systems which can not only perform simple work tasks, but also have elements of human self-developing psyche. Such AI-psyche could play an important role both in relations between psycho-robots and their owners as well as between psycho-robots. Since the presence of a huge numbers of psycho-complexes is an essential characteristic of human psychology, it would be interesting to model them in the AI-framework.

This paper presents the Computing Networks (CNs) framework. CNs are used to generalize neural and swarm architectures. Artificial neural networks, ant colony optimization, particle swarm optimization, and realistic biological models are used as examples of instantiations of CNs. The description of these architectures as CNs allows their comparison. Their differences and similarities allow the identification of properties that enable neural and swarm architectures to perform complex computations and exhibit complex cognitive abilities. In this context, the most relevant characteristics of CNs are the existence multiple dynamical and functional scales. The relationship between multiple dynamical and functional scales with adaptation, cognition (of brains and swarms) and computation is discussed.

This paper provides an accessible introduction to the cognitive systems paradigm of enaction and shows how it forms a practical framework for robotic systems that can develop cognitive abilities. The principal idea of enaction is that a cognitive system develops it own understanding of the world around it through its interactions with the environment. Thus, enaction entails that the cognitive system operates autonomously and that it generates its own models of how the world works. A discussion of the five key elements of enaction — autonomy, embodiment, emergence, experience, and sense-making — leads to a core set of functional, organizational, and developmental requirements which are then used in the design of a cognitive architecture for the iCub humanoid robot.