Appearance-based localization for mobile robots using digital zoom and visual compass

This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally

Evolutionary Algorithms for Reinforcement Learning

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications

A multi-modal study into students’ timing and learning regulation: time is ticking

Purpose This empirical study aims to demonstrate how the combination of trace data derived from technology-enhanced learning environments and self-response survey data can contribute to the investigation of self-regulated learning processes. Design/methodology/approach Using a showcase based on 1,027 students’ learning in a blended introductory quantitative course, the authors analysed the learning regulation and especially the timing of learning by trace data. Next, the authors connected these learning patterns with self-reports based on multiple contemporary social-cognitive theories. Findings The authors found that several behavioural facets of maladaptive learning orientations, such as lack of regulation, self-sabotage or disengagement negatively impacted the amount of practising, as well as timely practising. On the adaptive side of learning dispositions, the picture was less clear. Where some adaptive dispositions, such as the willingness to invest efforts in learning and self-perceived planning skills, positively impacted learning regulation and timing of learning, other dispositions such as valuing school or academic buoyancy lacked the expected positive effects. Research limitations/implications Due to the blended design, there is a strong asymmetry between what one can observe on learning in both modes. Practical implications This study demonstrates that in a blended setup, one needs to distinguish the grand effect on learning from the partial effect on learning in the digital mode: the most adaptive students might be less dependent for their learning on the use of the digital learning mode. Originality/value The paper presents an application of embodied motivation in the context of blended learning