1479779
Programming by demonstration: removing sub-optimal actions in a partially known configuration space
Programming by demonstration is a promising approach to automatic robot programming, however, methods are required to remove suboptimal actions that can be demonstrated by end users. In this paper we use the partial knowledge of configuration space (C-space) derived in the previous work by Chen et al. (2000) to remove suboptimal actions from a demonstration. Our idea is to use demonstrated paths to predict what regions in C-space are obstacle free. Suboptimal actions in a demonstration are then avoided by planning alternative actions that pass through the obstacle free regions. Experimental results show the validity of the approach. A demonstrated path containing significant sub-optimality was converted by the approach into a short, efficient path suitable for execution by the robot.
5704334
Making subsequence time series clustering meaningful
The startling claim was made that sequential time series clustering is meaningless. This has important consequences for a significant amount of work in the literature, since such a claim invalidates this work's contribution. In this paper, we show that sequential time series clustering is not meaningless, and that the problem highlighted in these works stem from their use of the Euclidean distance metric as the distance measure in the subsequence vector space. As a solution, we consider quite a general class of time series, and propose a regime based on two types of similarity that can exist between subsequence vectors, which give rise naturally to an alternative distance measure to Euclidean distance in the subsequence vector space. We show that, using this alternative distance measure, sequential time series clustering can indeed be meaningful. We repeat a key experiment in the work on which the "meaningless" claim was based, and show that our method leads to a successful clustering outcome.
14121852
Programming by demonstration - constructing task level plans in hybrid dynamic framework
This paper presents a novel approach to constructing a task level plan for an assembly task from demonstration. A hybrid dynamic system is chosen as an attractive way to model assembly tasks. We propose a framework where the mappings required within the hybrid dynamic system model are extracted from a demonstration. We extract the event path planner mapping, which determines a task level plan. Demonstrated paths are broken down into their base elements, called transitions. A path plan is then constructed from transitions that were well demonstrated. We test the approach by having a robot execute an assembly task using constructed task plans. The framework produces excellent results because: 1) the robot can perform selected paths better than when it simply copies the demonstrator, and 2) it allows flexible path selection so the robot can be given a disposition in how it executes the task.
13891792
Constructing Task-Level Assembly Strategies in Robot Programming by Demonstration
Programming by demonstration (PbD) is a technique for programming robots that holds much promise in making robots more accessible to ordinary, non-technical users. However, a well-known difficulty with the method is that a human will often demonstrate the task to be programmed inconsistently or even erroneously, leading to the inclusion of what is essentially noise in the demonstration. A number of techniques exist in the literature for filtering out this type of noise; however, most focus on very low level control command details. In this paper, we propose a new, complementary direction of research. We take a “task-level” view of the demonstration, and note that noise can exist at this level also. We propose a framework, based on a hybrid dynamic system modeling approach, to select the most optimal, task-level execution strategies that were demonstrated. We apply our framework to a real household task of inserting the compressible spindle of a paper towel holder into its supports. We conduct experiments to show that significant improvements in robot performance of the task can be achieved by a PbD regime that includes our method.
41316192
Efficient Learning of Pseudo-Boolean Functions from Limited Training Data
Pseudo-Boolean functions are generalizations of Boolean functions. We present a new method for learning pseudo-Boolean functions from limited training data. The objective of learning is to obtain a function f which is a good approximation of the target function f*. We define suitable criteria for the “goodness” of an approximating function. One criterion is to choose a function f that minimizes the “expected distance” with respect to a distance function d (over pairs of pseudo-Boolean functions) and the uniform distribution over all feasible pseudo-Boolean functions. We define two alternative “distance measures” over pairs of pseudo-Boolean functions, and show that they are are actually equivalent with respect to the criterion of minimal expected distance. We outline efficient algorithms for learning pseudo-Boolean functions according to these criteria. Other reasonable distance measures and “goodness” criteria are also discussed.
15937276
Web-based intuitive and effective design of symmetric tiles
This paper presents a new approach for intuitive and effective design of periodic symmetric tiles. We observe that planar graphs can effectively represent symmetric tiles and graph drawing provides an intuitive paradigm for designing symmetric tiles. Moreover, based on our theoretical work to represent hexagonal symmetry by rectangular symmetry, we are able to present all symmetric tiles as graphs embedded on a torus and based on simple modulo operations. This approach enables us to develop a simple and efficient algorithm, which has been implemented in Java. By using this software, designers, architects and artists can create interesting symmetric tiles directly on the web. We also have designed a few examples of symmetric tiles to show the effectiveness of the approach.
62212743
Resampling for face detection by self-adaptive genetic algorithm
Over the past ten years, face detection has been thoroughly studied in computer vision research for its interesting applications. However, all of the state-of-the-art statistical methods suffer from the data collection for training a classifier. This paper presents a self-adaptive genetic algorithm (GA)-based method to swell face database through re-sampling from the existing faces. The basic idea is that a face is composed of a limited components set, and the GA can simulate the procedure of heredity. This simulation can also cover the variations of faces in different lighting conditions, poses, accessories, and quality conditions. To verify the generalization capability of the proposed method, we also use the expanded database to train an Adaboost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be efficiently speeded up by the proposed methods.
10853556
A robust watermarking method based on wavelet and Zernike transform
As watermarking is increasingly applied for a variety of applications, various properties of watermarks, such as how they respond to common signal transformations or deliberate attacks, have become important concerns. In this paper, we propose a new approach to embed a watermark that is to modify the block average in wavelet domain. If the watermarked image has been attacked by geometric ways, we can figure it out by Zernike transform and then retrieve it. Experiments prove that the method is not only robust to geometric attacks, but also to JPEG and Checkmark attacks.
1250825
Enhancing Training Set for Face Detection
We present a novel method to enhance training set for face detection with nonlinearly generated examples from the original data. The motivation is from support vector machines (SVM) that, for classification problems, examples lying close to class boundary usually have more influence and thus are more informative than those far from the boundary. We utilize a nonlinear technique - reduced set (RS) method and a new image distance metric to generate new examples, and then add them to the original collected database to enhance it. Extensive experiments show that the proposed approach has an encouraging performance