13,929 research outputs found
Vision-based deep execution monitoring
Execution monitor of high-level robot actions can be effectively improved by
visual monitoring the state of the world in terms of preconditions and
postconditions that hold before and after the execution of an action.
Furthermore a policy for searching where to look at, either for verifying the
relations that specify the pre and postconditions or to refocus in case of a
failure, can tremendously improve the robot execution in an uncharted
environment. It is now possible to strongly rely on visual perception in order
to make the assumption that the environment is observable, by the amazing
results of deep learning. In this work we present visual execution monitoring
for a robot executing tasks in an uncharted Lab environment. The execution
monitor interacts with the environment via a visual stream that uses two DCNN
for recognizing the objects the robot has to deal with and manipulate, and a
non-parametric Bayes estimation to discover the relations out of the DCNN
features. To recover from lack of focus and failures due to missed objects we
resort to visual search policies via deep reinforcement learning
Encouraging Quality Regulation: Theories and Tools
Achieving good regulatory outcomes normally requires high quality design, implementation and review of the regulatory regime. Major regulatory theories focus on concepts such as the public interest, the role of interest groups, and regulatory capture to explain why regulations come into existence. Regulatory design, however, exists at two levels. Downstream design involves creating a regime to give the appropriate incentives to firms and consumers. Upstream design seeks to incentivise regulators themselves to create and operate high quality regulatory regimes. This paper focuses on the latter. The OECD has undertaken a major programme on regulatory governance to ensure quality in the design and implementation of regulations. Such measures are now widespread. New Zealand has gradually implemented these approaches including Regulatory Impact Analysis (RIA) in its decision- making processes. These measures are supported, and to some degree required, by increased interaction with Australian practices through institutions such as the Council of Australian Governments and the obligations of Trans-Tasman Mutual Recognition Arrangement. Achieving full integration of best practice that creates an environment for consistently delivering high quality regulation requires a broad and sustained focus on design, capability, incentives and follow-up. New Zealand has attempted over the last decade to improve regulatory outcomes by focusing on the incentives on regulators. There is still scope for further improvement. Sustained progress on a number of mutually supporting initiatives, with continued reinforcement of the underlying messages and careful building of the necessary institutions and practices is required for continued improvement.Quality regulation; Regulatory Quality; Regulatory Reform; Regulatory Capture
Panel: Why do we toil? Benefiting research at the cost of practice or vice versa?
In this paper, we present a systematic literature review in the field of IT Outsourcing with a focus on
risk management. The source material of the review consists of 97 high quality journal articles
published in 18 journals between 2001 and September 2008. Besides an analysis of related work, this
review provides an overview of applied research methods and theories in the field of IT Outsourcing.
The articles are then analyzed from a risk management point of view to highlight key risk factors and
their specific impact on IT Outsourcing. Identified risk factors are further analyzed in order to assign
each risk factor to the phases of a typical IT Outsourcing process (life-cycle). The results of the review
show that empirical research is the most applied method and that action research and reference
modelling have not been used at all so far. Furthermore, elements of a research agenda are discussed
in order to determine further steps to the construction of a reference model for risk management in IT
Outsourcing. This paper mainly aims at an audience of experienced researchers in the field of IT
Outsourcing who are looking for research ideas and at junior scientists (e.g. PhD students) entering
this emerging field of research
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Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping
Mobile robots need accurate, high fidelity models of their operating environments in order to complete their tasks safely and efficiently. Generating these models is most often done via Simultaneous Localization and Mapping (SLAM), a paradigm where the robot alternatively estimates the most up-to-date model of the environment and its position relative to this model as it acquires new information from its sensors over time. Because robots operate in many different environments with different compute, memory, sensing, and form constraints, the nature and quality of information available to individual instances of different SLAM systems varies substantially. `One-size-fits-all\u27 solutions are thus exceedingly difficult to engineer, and highly specialized systems, which represent the state-of-the-art for most types of deployments, are not robust to operating conditions in which their assumptions are not met. This thesis seeks to investigate an alternative approach to these robustness and universality problems by incorporating existing SLAM solutions within a larger framework supported by planning and learning. The central idea is to combine learned models that estimate SLAM algorithm performance under a variety of sensory conditions, in this case neural networks, with planners designed for planning under uncertainty and partial observability, in this case partially observable Markov decision problems (POMDPs). Models of existing SLAM algorithms can be learned, and these models can then be used online to estimate the performance of a range of solutions to the SLAM problem at hand. The POMDP policy then selects the appropriate algorithm, given the estimated performance, cost of switching methods, and other information. This general approach may also be applicable to many other robotics problems that rely on data-fusion, such as grasp planning, motion planning, or object identification
WEB BASED LABORATORY TASK-SUBMITTER APPLICATION MODEL
Teaching learning process in laboratory is obligatory in engineering education especially for course in information technology (IT). Â To make laboratory activities become more interesting for the students, lecturers must build application-based exercise for the student, but after students accomplished their short exercise, it is found that lecturers got difficulty to compile and grade all the exercises. This paper is based on the idea how to overcome the problem above, in order to make activities in the laboratory comfortable for both lectures and student. It is expected that the lectures will able to fully control all practical activity and save the time automatically. To make approach to solve the problem in IT, a modeling process must be conducted first. There for, firstly, this paper explain the model approach for the problem above, then the IT design for practices in laboratory is described. The IT design to overcome the problem has been effectively applied in the real teaching learning process
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