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An essential measure of autonomy in assistive service robots is adaptivity to the various contexts of human-oriented tasks, which are subject to subtle variations in task parameters that determine optimal behaviour. In this work, we propose an apprenticeship learning approach to achieving context-aware action generalization on the task of robot-to-human object hand-over. The procedure combines learning from demonstration and reinforcement learning: a robot first imitates a demonstrator’s execution of the task and then learns contextualized variants of the demonstrated action through experience. We use dynamic movement primitives as compact motion representations, and a model-based C-REPS algorithm for learning policies that can specify hand-over position, conditioned on context variables. Policies are learned using simulated task executions, before transferring them to the robot and evaluating emergent behaviours. We additionally conduct a user study involving participants assuming different postures and receiving an object from a robot, which executes hand-overs by either imitating a demonstrated motion, or adapting its motion to hand-over positions suggested by the learned policy. The results confirm the hypothesized improvements in the robot’s perceived behaviour when it is context-aware and adaptive, and provide useful insights that can inform future developments.
Machine learning-based solutions are frequently adapted in several applications that require big data in operations. The performance of a model that is deployed into operations is subject to degradation due to unanticipated changes in the flow of input data. Hence, monitoring data drift becomes essential to maintain the model’s desired performance. Based on the conducted review of the literature on drift detection, statistical hypothesis testing enables to investigate whether incoming data is drifting from training data. Because Maximum Mean Discrepancy (MMD) and Kolmogorov-Smirnov (KS) have shown to be reliable distance measures between multivariate distributions in the literature review, both were selected from several existing techniques for experimentation. For the scope of this work, the image classification use case was experimented with using the Stream-51 dataset. Based on the results from different drift experiments, both MMD and KS showed high Area Under Curve values. However, KS exhibited faster performance than MMD with fewer false positives. Furthermore, the results showed that using the pre-trained ResNet-18 for feature extraction maintained the high performance of the experimented drift detectors. Furthermore, the results showed that the performance of the drift detectors highly depends on the sample sizes of the reference (training) data and the test data that flow into the pipeline’s monitor. Finally, the results also showed that if the test data is a mixture of drifting and non-drifting data, the performance of the drift detectors does not depend on how the drifting data are scattered with the non-drifting ones, but rather their amount in the test set
The design of an efficient digital circuit in term of low-power has become a very challenging issue. For this reason, low-power digital circuit design is a topic addressed in electrical and computer engineering curricula, but it also requires practical experiments in a laboratory. This PhD research investigates a novel approach, the low-power design laboratory system by developing a new technical and pedagogical system. The low-power design laboratory system is composed of two types of laboratories: the on-site (hands-on) laboratory and the remote laboratory. It has been developed at the Bonn-Rhine-Sieg University of Applied Sciences to teach low-power techniques in the laboratory. Additionally, this thesis contributes a suggestion on how the learning objectives can be complemented by developing a remote system in order to improve the teaching process of the low-power digital circuit design. This laboratory system enables online experiments that can be performed using physical instruments and obtaining real data via the internet. The laboratory experiments use a Field Programmable Gate Array (FPGA) as a design platform for circuit implementation by students and use image processing as an application for teaching low-power techniques.
This thesis presents the instructions for the low-power design experiments which use a top-down hierarchical design methodology. The engineering student designs his/her algorithm with a high level of abstraction and the experimental results are obtained and measured at a low level (hardware) so that more information is available to correctly estimate the power dissipation such as specification, latency, thermal effect, and technology used. Power dissipation of the digital system is influenced by specification, design, technology used, as well as operating temperature. Digital circuit designers can observe the most influential factors in power dissipation during the laboratory exercises in the on-site system and then use the remote system to supplement investigating the other factors. Furthermore, the remote system has obvious benefits such as developing learning outcomes, facilitating new teaching methods, reducing costs and maintenance, cost-saving by reducing the numbers of instructors, saving instructor time and simplifying their tasks, facilitating equipment sharing, improving reliability, and finally providing flexibility of usage the laboratories.
For many different applications, current information about the bandwidth-related metrics of the utilized connection is very useful as they directly impact the performance of throughput sensitive applications such as streaming servers, IPTV and VoIP applications. In literature, several tools have been proposed to estimate major bandwidth-related metrics such as capacity, available bandwidth and achievable throughput. The vast majority of these tools fall into one of Packet Pair (PP), Variable Packet Size (VPS), Self-Loading of Periodic Streams (SLoPS) or Throughput approaches. In this study, seven popular bandwidth estimation tools including nettimer, pathrate, pathchar, pchar, clink, pathload and iperf belonging to these four well-known estimation techniques are presented and experimentally evaluated in a controlled testbed environment. Differently from the rest of studies in literature, all tools have been uniformly classified and evaluated according to an objective and sophisticated classification and evaluation scheme. The performance comparison of the tools incorporates not only the estimation accuracy but also the probing time and overhead caused.