Open Access

In Artificial Intelligence, numerous learning paradigms have been developed over the past decades. In most cases of embodied and situated agents, the learning goal for the artificial agent is to „map“ or classify the environment and the objects therein [1, 2], in order to improve navigation or the execution of some other domain-specific task. Dynamic environments and changing tasks still pose a major challenge for robotic learning in real-world domains. In order to intelligently adapt its task strategies, the agent needs cognitive abilities to more deeply understand its environment and the effects of its actions. In order to approach this challenge within an open-ended learning loop, the XPERO project (http://www.xpero.org) explores the paradigm of Learning by Experimentation to increase the robot's conceptual world knowledge autonomously. In this setting, tasks which are selected by an actionselection mechanism are interrupted by a learning loop in those cases where the robot identifies learning as necessary for solving a task or for explaining observations. It is important to note that our approach targets unsupervised learning, since there is no oracle available to the agent, nor does it have access to a reward function providing direct feedback on the quality of its learned model, as e.g. in reinforcement learning approaches. In the following sections we present our framework for integrating autonomous robotic experimentation into such a learning loop. In section 1 we explain the different modules for stimulation and design of experiments and their interaction. In section 2 we describe our implementation of these modules and how we applied them to a real world scenario to gather target-oriented data for learning conceptual knowledge. There we also indicate how the goaloriented data generation enables machine learning algorithms to revise the failed prediction model.

Robot deployment in realistic dynamic environments is a challenging problem despite the fact that robots can be quite skilled at a large number of isolated tasks. One reason for this is that robots are rarely equipped with powerful introspection capabilities, which means that they cannot always deal with failures in a reasonable manner; in addition, manual diagnosis is often a tedious task that requires technicians to have a considerable set of robotics skills.

To achieve autonomous mobile robot navigation, one of fundamental requirements is localization technique. It is difficult for any mobile robot to perform assigned task or sequence of tasks autonomously without accurate position knowledge relative to its environment. There are many existing approaches to accomplish localization in mobile robots but some of the existing mobile robot localization techniques can not be used in large indoor environments because of installation and environmental requirements. Many of existing indoor localization solutions costs significantly more as compared to the solution evaluated in this paper. In this paper a novel passive landmark based indoor position estimation technique is investigated for its robustness, accuracy, repeatability and reliability in large public environment. Use of passive artificial landmarks minimize the disturbance to the user, make the installation process easy, scalable and reduces the total system cost. A comprehensive testing arena is developed at Computer Science Department, University of Applied Sciences Bonn-Rhein-Sieg. This technique has been tested here under different environmental conditions i.e different lighting conditions, different sensor angles, different orientations and different ceiling heights. These tests are designed to subject this technique to possible unexpected and expected conditions that could arise in any large indoor public environment e.g supermarkets, museums etc. This makes our testing and experimental evaluation highly valuable before integrating this technique with mobile service robots at a large scale. This paper experimentally evaluates reliability, robustness and accuracy of this technique over other techniques.

With the increasing average age of the population in many developed countries, afflictions like cardiovascular diseases have also increased. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract this development. To avoid overstrain, determining an optimal training dose is crucial. In previous research, heart rate has been shown to be a good measure for cardiovascular behavior. Hence, prediction of the heart rate from work load information is an essential part in models used for training control. Most heart-rate-based models are described in the context of specific scenarios, and have been evaluated on unique datasets only. In this paper, we conduct a joint evaluation of existing approaches to model the cardiovascular system under a certain strain, and compare their predictive performance. For this purpose, we investigated some analytical models as well as some machine learning approaches in two scenarios: prediction over a certain time horizon into the future, and estimation of the relation between work load and heart rate over a whole training session.