waashk/dblp huggingface dataset:
robot training using system identification. this paper focuses on developing a formal, theory-based design methodology to generate transparent robot control programs using mathematical functions. the research finds its theoretical roots in robot training and system identification techniques such as armax (auto-regressive moving average models with exogenous inputs) and narmax (non-linear armax). these techniques produce linear and non-linear polynomial functions that model the relationship between a robot's sensor perception and motor response. the main benefits of the proposed design methodology, compared to the traditional robot programming techniques are: (i) it is a fast and efficient way of generating robot control code, (ii) the generated robot control programs are transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour, and (iii) it requires very little explicit knowledge of robot programming, therefore end-users/programmers who do not have any specialized robot programming skills can nevertheless generate task-achieving sensor-motor couplings. the nature of this research is concerned with obtaining sensor-motor couplings, be it through human demonstration via the robot, direct human demonstration, or other means. the viability of our methodology has been demonstrated by teaching various mobile robots different sensor-motor tasks such as wall following, corridor passing, door traversal and route learning.
unsupervised identification of useful visual landmarks using multiple segmentations and top-down feedback. in this paper, we tackle the problem of unsupervised selection and posterior recognition of visual landmarks in image sequences acquired by an indoor mobile robot. this is a highly valuable perceptual capability for a wide variety of robotic applications, in particular autonomous navigation. our method combines a bottom-up data driven approach with top-down feedback provided by high level semantic representations. the bottom-up approach is based on three main mechanisms: visual attention, area segmentation, and landmark characterization. as there is no segmentation method that works properly in every situation, we integrate multiple segmentation algorithms in order to increase robustness of the approach. in terms of top-down feedback, this is provided by two information sources: (i) an estimation of the robot position that reduces the searching scope for potential matches with previously selected landmarks, (ii) a set of weights that, according to the results of previous recognitions, controls the influence of each segmentation algorithm in the recognition of each landmark. we test our approach with encouraging results in three datasets corresponding to real-world scenarios.
autonomous functional configuration of a network robot system. we consider distributed systems of networked robots in which: (1) each robot includes sensing, acting and/or processing modular functionalities; and (2) robots can help each other by offering those functionalities. a functional configuration is any way to allocate and connect functionalities among the robots. an interesting feature of a system of this type is the possibility to use different functional configurations to make the same set of robots perform different tasks, or to perform the same task under different conditions. in this paper, we propose an approach to automatically generate at run time a functional configuration of a network robot system to perform a given task in a given environment, and to dynamically change this configuration in response to failures. our approach is based on artificial intelligence planning techniques, and it is provably sound, complete and optimal. moreover, our configuration planner can be combined with an action planner to deal with tasks that require sequences of configurations. we illustrate our approach on a specific type of network robot system, called peis-ecology, and show experiments in which a sequence of configurations is automatically generated and executed on real robots. these experiments demonstrate that our self-configuration approach can help the system to achieve greater autonomy, flexibility and robustness.
a probabilistic framework for entire wsn localization using a mobile robot. this paper presents a new method for the localization of a wireless sensor network (wsn) by means of collaboration with a robot within a network robot system (nrs). the method employs the signal strength as input, and has two steps: an initial estimation of the position of the nodes is obtained centrally by one robot and is based on particle filtering. it does not require any prior information about the position of the nodes. in the second stage, the nodes refine their position estimates employing a decentralized information filter. the paper shows how the method is able to recover the 3d position of the nodes, and is very suitable for wsn outdoor applications. the paper includes several implementation aspects and experimental results.
online generation of cyclic leg trajectories synchronized with sensor measurement. the generation of trajectories for a biped robot is a problem which has been largely studied for several years, and many satisfying offline solutions exist for steady-state walking in absence of disturbances. the question is a little more complex when the generation of the desired trajectories of joints or links has to be achieved or adapted online, i.e. in real time, for example when it is wished to strongly synchronize these trajectories with an external motion. this is precisely the problem addressed in this paper. indeed, we consider the case where the ''master'' motion is measured by a position sensor embedded on a human leg. we propose a method to synchronize the motion of a robot or of other device with respect to the output signal of the sensor. the main goal is to estimate as accurately as possible the current phase along the gait cycle. we use for that purpose a model based on a nonlinear oscillator, which we associate an observer. introducing the sensor output in the observer allows us to compute the oscillator phase and to generate a synchronized multilinks trajectory, at a very low computational cost. the paper also presents evaluation results in terms of robustness against parameter estimation errors and velocity changes in the input.
model identification and model analysis in robot training. robot training is a fast and efficient method of obtaining robot control code. many current machine learning paradigms used for this purpose, however, result in opaque models that are difficult, if not impossible to analyse, which is an impediment in safety-critical applications or application scenarios where humans and robots occupy the same workspace. in experiments with a magellan pro mobile robot we demonstrate that it is possible to obtain transparent models of sensor-motor couplings that are amenable to subsequent analysis, and how such analysis can be used to refine and tune the models post hoc.
local visual homing by warping of two-dimensional images. local visual homing methods can be used in the context of topological maps to travel between neighboring locations. these methods take two images as input and produce a home vector that points from the vantage point of one image to that of the other. ''warping'' [m.o. franz, b. scholkopf, h.a. mallot, h.h. bulthoff, where did i take that snapshot? scene-based homing by image matching, biological cybernetics 79 (3) (1998) 191-202] is an attractive homing method since it does not require an external compass. here we describe how the performance of warping can be substantially improved by extending the method from one- to two-dimensional images, with only a moderate increase in the computational effort. experiments on several image databases confirm the improved performance.
ivvi: intelligent vehicle based on visual information. human errors are the cause of most traffic accidents, with drivers' inattention and wrong driving decisions being the two main sources. these errors can be reduced, but not completely eliminated. that is why advanced driver assistance systems (adas) can reduce the number, danger and severity of traffic accidents. several adas, which nowadays are being researched for intelligent vehicles, are based on artificial intelligence and robotics technologies. in this article a research platform for the implementation of systems based on computer vision is presented, and different visual perception modules useful for some adas such as line keeping system, adaptive cruise control, pedestrian protector, or speed supervisor, are described.
online generation of scene descriptions in urban environments. the ability to extract a rich set of semantic workspace labels from sensor data gathered in complex environments is a fundamental prerequisite to any form of semantic reasoning in mobile robotics. in this paper, we present an online system for the augmentation of maps of outdoor urban environments with such higher-order, semantic labels. the system employs a shallow supervised classification hierarchy to classify scene attributes, consisting of a mixture of 2d/3d geometric and visual scene information, into a range of different workspace classes. the union of classifier responses yields a rich, composite description of the local workspace. we present extensive experimental results, using two large urban data sets collected by our research platform.
subjective local maps for hybrid metric-topological slam. hybrid maps where local metric submaps are kept in the nodes of a graph-based topological structure are gaining relevance as the focus of robot simultaneous localization and mapping (slam) shifts towards spatial scalability and long-term operation. in this paper we examine the applicability of spectral graph partitioning techniques to the automatic generation of metric submaps by establishing groups in the sequence of observations gathered by the robot. one of the main aims of this work is to provide a probabilistically grounded interpretation of such a partitioning technique in the context of generating local maps. we also discuss how to apply it to different kinds of sensory data (landmarks extracted from stereo images and laser range scans) and how to consider them simultaneously. an important feature of our approach is that the partitioning takes into account the intrinsic characteristics of the sensors, such as the sensor field of view, instead of applying heuristics supplied by a human as in other works. thus the robot builds ''subjective'' local maps whose size will be determined by the nature of the sensors. the ideas presented here are supported by experimental results from a real mobile robot as well as simulations for statistical analysis. we discuss the effects of considering different combinations of sensors in the resulting clustering of the environment.
a new autonomous celestial navigation method for the lunar rover. a secure and autonomous navigation system is needed for the lunar rover in future lunar missions in case of emergencies. celestial navigation is a very attractive solution for long distance navigation on the moon without the need of ground navigation aids. it only uses star altitudes, which are measured by a high accuracy star sensor and inertial measurement unit (imu) to estimate the position of the rover. the navigational accuracy of this method depends largely on the accuracy of measurements, so the measurement errors have a great impact on the navigational performance. a new autonomous celestial navigation method for the lunar rover is presented in this paper, which uses the augmented state unscented particle filter (asupf) to deal with the systematic error and random error in the measurements. the validity and feasibility of this new method is tested and examined by the hardware-in-loop test. a position estimation error within 60 m is obtained. compared to the conventional method, this method shows better navigation performance and higher adaptability to these measurement errors.
a bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits. this paper proposes an efficient, bezier curve based approach for the path planning of a mobile robot in a multi-agent robot soccer system. the boundary conditions required for defining the bezier curve are compatible with the estimated initial state of the robot and the ball. the velocity of the robot along the path is varied continuously to its maximum allowable levels by keeping its acceleration within the safe limits. an obstacle avoidance scheme is incorporated for dealing with the stationary and moving obstacles. when the robot is approaching a moving obstacle in the field, it is decelerated and deviated to another bezier path leading to the estimated target position. the radius of curvature of the path at its end points is determined from the known terminal velocity constraint of the robot.
kdd-2004 workshop report link analysis and group detection (linkkdd-2004). in this paper we provide a summary of the workshop on link analysis and group detection (linkkdd-2004) held in conjunction with acm sigkdd 2004, on august 22, seattle, washington, usa. we report in details about the research issues addressed in the talks and the workshop.
kdd-2002 workshop report fractals and self-similarity in data mining: issues and approaches. in this report we provide a summary of the first workshop on application of self-similarity and fractals in data mining: issues and approaches held in conjunction with acm sigkdd 2002, july 23 at edmonton, alberta, canada.
feature engineering for a gene regulation prediction task. this paper describes an approach that won honorable mention for the gene regulation prediction task of the 2002 kdd cup competition [1]. our methodology used extensive cross-validation to direct the search for an appropriate problem representation and the selection of an 'off-the-shelf' induction algorithm. a prominent trait of the dataset is the presence of three hierarchical attributes, for each of which we generated a novel predictive feature: the percentage of positives hierarchically aggregated at the node specified by the instance.
kdd-2005 workshop report: link discovery: issues, approaches and application (linkkdd-2005). in this paper we provide a summary of the workshop on link discovery: issues, approaches and applications (linkkdd-2005) held in conjunction with acm sigkdd 2005, on august 21st in chicago, illinois, usa. we report in detail about the research issues addressed in the talks at the workshop.
towards long pattern generation in dense databases. this paper discusses the problem of long pattern generation in dense databases. in recent years, there has been an increase of interest in techniques for maximal pattern generation. we present a survey of this class of methods for long pattern generation which differ considerably from the level-wise approach of traditional methods. many of these techniques are rooted in combinatorial tricks which can be applied only when the generation of frequent patterns is not forced to be level wise. we present an overview of the different kinds of methods which can be used in order to improve the counting and search space exploration methods for long patterns.
overview of the 2003 kdd cup. this paper surveys the 2003 kdd cup, a competition held in conjunction with the ninth acm sigkdd international conference on knowledge discovery and data mining (kdd) in august 2003. the competition focused on mining the complex real-life social network inherent in the e-print arxiv (arxiv.org). we describe the four kdd cup tasks: citation prediction, download prediction, data cleaning, and an open task.
towards effective and interpretable data mining by visual interaction. the primary aim of most data mining algorithms is to facilitate the discovery of concise and interpretable information from large amounts of data. however, many of the current formalizations of data mining algorithms have not quite reached this goal. one of the reasons for this is that the focus on using purely automated techniques has imposed several constraints on data mining algorithms. for example, any data mining problem such as clustering or association rules requires the specification of particular problem formulations, objective functions, and parameters. such systems fail to take the user's needs into account very effectively. this makes it necessary to keep the user in the loop in a way which is both efficient and interpretable. one unique way of achieving this is by leveraging human visual perceptions on intermediate data mining results. such a system combines the computational power of a computer and the intuitive abilities of a human to provide solutions which cannot be achieved by either. this paper will discuss a number of recent approaches to several data mining algorithms along these lines.
kdd'99 competition: knowledge discovery contest. in this paper, we expand on the 1998 kdd cup competition findings: exploratory data analysis reveals unusual data anomalies; a two-stage prediction model yields superior results those obtained in the 1998 competition; we use a decision tree better understand the model (the decision boundary); and we apply a confidence interval to establish a range upon which we can reasonably judge model performance.
a note on "beyond market baskets: generalizing association rules to correlations". in their paper [1], s. brin, r. matwani and c. silverstien discussed measuring significance of (generalized) association rules via the support and the chi-squared test for correlation. they provided some illustrative examples and pointed that the chi-squared test needs to be agumented by a measure of interest that they also suggested.this paper presents a further elaboration and extension of their discussion. as suggested by brin et al, the chi-squared test succeeds in measuring the cell dependencies in a 2x2 contingency table. however, it can be misleading in cases of bigger contingency tables. we will give some illustrative examples based on those presented in [1]. we will also propose a more appropriate reliability measure of association rules.
link mining: a new data mining challenge. a key challenge for data mining is tackling the problem of mining richly structured datasets, where the objects are linked in some way. links among the objects may demonstrate certain patterns, which can be helpful for many data mining tasks and are usually hard to capture with traditional statistical models. recently there has been a surge of interest in this area, fueled largely by interest in web and hypertext mining, but also by interest in mining social networks, security and law enforcement data, bibliographic citations and epidemiological records.
sampling algorithms for pure network topologies: a study on the stability and the separability of metric embeddings. in a time of information glut, observations about complex systems and phenomena of interest are available in several applications areas, such as biology and text. as a consequence, scientists have started searching for patterns that involve interactions among the objects of analysis, to the effect that research on models and algorithms for network analysis has become a central theme for knowledge discovery and data mining (kdd). the intuitions behind the plethora of approaches rely upon few basic types of networks, identified by specific local and global topological properties, which we term "pure" topology types.in this paper, (1) we survey pure topology types along with existing sampling algorithms that generate them, (2) we introduce novel algorithms that enhance the diversity of samples, and address the case of cellular topologies, (3) we perform statistical studies of the stability of the properties of pure types to alternative generative algorithms, and a joint study of the separability of pure types, in terms of their embedding in a space of metrics for network analysis, widely adopted in the social and physical sciences.we conclude with a word of caution to the practitioners, who sample pure topology types to assess the "statistical significance" of their findings, e.g., the p-value of the clustering coefficient is sensitive to the sampling algorithm used. we find that different pure types share similar topological properties. further, real world networks hardly present the variability profile of a single pure type. we suggest the assumption of "mixtures of types" as an alternative starting point for developing models and algorithms for network analysis.
introduction to the special issue on link mining. an emerging challenge for data mining is the problem of mining richly structured datasets, where the objects are linked in some way. many real-world datasets describe a variety of entity types linked via multiple types of relations. these links provide additional context that can be helpful for many data mining tasks. yet multi-relational data violates the traditional assumption of independent, identically distributed data instances that provides the basis for many statistical machine learning algorithms. therefore, new approaches are needed that can exploit the dependencies across the attribute and link structure.
link mining: a survey. many datasets of interest today are best described as a linked collection of interrelated objects. these may represent homogeneous networks, in which there is a single-object type and link type, or richer, heterogeneous networks, in which there may be multiple object and link types (and possibly other semantic information). examples of homogeneous networks include single mode social networks, such as people connected by friendship links, or the www, a collection of linked web pages. examples of heterogeneous networks include those in medical domains describing patients, diseases, treatments and contacts, or in bibliographic domains describing publications, authors, and venues. link mining refers to data mining techniques that explicitly consider these links when building predictive or descriptive models of the linked data. commonly addressed link mining tasks include object ranking, group detection, collective classification, link prediction and subgraph discovery. while network analysis has been studied in depth in particular areas such as social network analysis, hypertext mining, and web analysis, only recently has there been a cross-fertilization of ideas among these different communities. this is an exciting, rapidly expanding area. in this article, we review some of the common emerging themes.