Advantages and limitations of reservoir computing on model learning for robot control

In certain cases analytical derivation of physicsbased models of robots is difficult or even impossible. A potential workaround is the approximation of robot models fromsensor data-streams employing machine learning approaches.In this paper, the inverse dynamics models are learned byemploying a learning algorithm, introduced in [1], which isbased on reservoir computing in conjunction with self-organizedlearning and Bayesian inference. The algorithm is evaluatedand compared to other state of the art algorithms in termsof generalization ability, convergence and adaptability usingfive datasets gathered from four robots in order to investigateits pros and cons. Results show that the proposed algorithmcan adapt in real-time changes of the inverse dynamics modelsignificantly better than the other state of the art algorithms

Human-Machine Interface for Remote Training of Robot Tasks

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the "remote" robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.Comment: Accepted in IEEE International Conference on Imaging Systems and Techniques - IST201

Robust Uncertainty Estimation for Classification of Maritime Objects

We explore the use of uncertainty estimation in the maritime domain, showing the efficacy on toy datasets (CIFAR10) and proving it on an in-house dataset, SHIPS. We present a method joining the intra-class uncertainty achieved using Monte Carlo Dropout, with recent discoveries in the field of outlier detection, to gain more holistic uncertainty measures. We explore the relationship between the introduced uncertainty measures and examine how well they work on CIFAR10 and in a real-life setting. Our work improves the FPR95 by 8% compared to the current highest-performing work when the models are trained without out-of-distribution data. We increase the performance by 77% compared to a vanilla implementation of the Wide ResNet. We release the SHIPS dataset and show the effectiveness of our method by improving the FPR95 by 44.2% with respect to the baseline. Our approach is model agnostic, easy to implement, and often does not require model retraining

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Study and implementation of stereo vision systems for robotic applications

Stereo vision has been chosen by natural selection as the most common way to estimate the depth ofobjects. A pair of two-dimensional images is enough in order to retrieve the third dimension of thescene under observation. The importance of this method is great, apart from the living creatures, forsophisticated machine systems, as well. During the last years robotics has made significant progressand the state of the art is now about achieving autonomous behaviors. In order to accomplishthe target of robots being able to move and act autonomously, accurate representations of theirenvironments are required. Both these fields, stereo vision and accomplishing autonomous roboticbehaviors, have been in the center of this PhD thesis. The issue of robots using machine stereovision is not a new one. The number and significance of the researchers that have been involved,as well as the publishing rate of relevant scientific papers indicates an issue that is interesting andstill open to solutions and fresh ideas rather than a banal and solved issue.The motivation of this PhD thesis has been the observation that the combination of stereo visionusage and autonomous robots is usually performed in a simplistic manner of simultaneously usingtwo independent technologies. This situation is owed to the fact that the two technologies haveevolved independently and by different scientific communities. Stereo vision has mainly evolvedwithin the field of computer vision. On the other hand, autonomous robots are a branch of therobotics and mechatronics field. Methods that have been proposed within the frame of computervision are not generally satisfactory for use in robotic applications. This fact is due to that anautonomous robot places strict constraints concerning the demanded speed of calculations and theavailable computational resources. Moreover, their inefficiency is commonly owed to factors relatedto the environments and the conditions of operation. As a result, the used algorithms, in this casethe stereo vision algorithms, should take into consideration these factors during their development.The required compromises have to retain the functionality of the integrated system.The objective of this PhD thesis is the development of stereo vision systems customized for use inautonomous robots. Initially, a literature survey was conducted concerning stereo vision algorithmsand corresponding robotic applications. The survey revealed the state of the art in the specificfield and pointed out issues that had not yet been answered in a satisfactory manner. Afterwards,novel stereo vision algorithms were developed, which satisfy the demands posed by robotic systemsand propose solutions to the open issues indicated by the literature survey. Finally, systems thatembody the proposed algorithms and treat open robotic applications’ issues have been developed.Within this dissertation there have been used for the first time and combined in a novel wayvarious computational tools and ideas originating from different scientific fields. There have been used biologically and psychologically inspired methods, such as the logarithmic response law (Weber-Fechner law) and the gestalt laws of perceptual organization (proximity, similarity and continuity).Furthermore, there have been used sophisticated computational methods, such as 2D and 3D cellularautomata and fuzzy inference systems for computer vision applications. Additionally, ideas from thefield of video coding have been incorporated in stereo vision applications. The resulting methodshave been applied to basic computer vision depth extraction applications and even to advancedautonomous robotic behaviors.In more detail, the possibility of implementing effective hardware-implementable stereo correspondencealgorithms has been investigated. Specifically, an algorithm that combines rapid execution,simple and straight-forward structure, as well as high-quality of results is presented. Thesefeatures render it as an ideal candidate for hardware implementation and for real-time applications.The algorithm utilizes Gaussian aggregation weights and 3D cellular automata in order to achievehigh-quality results. This algorithm comprised the basis of a multi-view stereo vision system. Thefinal depth map is produced as a result of a certainty assessment procedure. Moreover, a new hierarchicalcorrespondence algorithm is presented, inspired by motion estimation techniques originallyused in video encoding. The algorithm performs a 2D correspondence search using a similar hierarchicalsearch pattern and the intermediate results are refined by 3D cellular automata. Thisalgorithm can process uncalibrated and non-rectified stereo image pairs, maintaining the computationalload within reasonable levels. It is well known that non-ideal environmental conditions,such as differentiations in illumination depending on the viewpoint heavily affect the stereo algorithms’performance. In this PhD thesis a new illumination-invariant pixels’ dissimilarity measureis presented that can substitute the established intensity-based ones. The proposed measure can beadopted by almost any of the existing stereo algorithms, enhancing them with its robust features.The algorithm using the proposed dissimilarity measure has outperformed all the other examinedalgorithms, exhibiting tolerance to illumination differentiations and robust behavior. Moreover, anovel stereo correspondence algorithm that incorporates many biologically and psychologically inspiredfeatures to an adaptive weighted sum of absolute differences framework is presented. Inaddition to ideas already exploited, such as the color information utilization, gestalt laws of proximityand similarity, new ones have been adopted. The algorithm introduces the use of circularsupport regions, the gestalt law of continuity, as well as the psychophysically-based logarithmic responselaw. All the aforementioned perceptual tools act complementarily inside a straight-forwardcomputational algorithm.Furthermore, stereo correspondence algorithms have been further exploited as the basis of moreadvanced robotic behaviors. Vision-based obstacle avoidance algorithms for autonomous mobilerobots are presented. These algorithms avoid, as much as possible, computationally complex processes.The only sensor required is a stereo camera. The algorithms consist of two building blocks.The first one is a stereo algorithm, able to provide reliable depth maps of the scenery in frame ratessuitable for a robot to move autonomously. The second building block is either a simple decisionmakingalgorithm or a fuzzy logic-based one, which analyze the depth maps and deduce the mostappropriate direction for the robot to avoid any existing obstacles. Finally, a visual SimultaneousLocalization and Mapping (SLAM) algorithm suitable for indoor applications is proposed. Thealgorithm is focused on computational effectiveness and the only sensor used is a stereo cameraplaced onboard a moving robot. The algorithm processes the acquired images calculating the depthof the scenery, detecting occupied areas and progressively building a map of the environment. Thestereo vision-based SLAM algorithm embodies a custom-tailored stereo correspondence algorithm,the robust scale and rotation invariant feature detection and matching "Speeded Up Robust Features" (SURF) method, a computationally effective v-disparity image calculation scheme, a novelmap-merging module, as well as a sophisticated cellular automata-based enhancement stage