2164421
Generating a configuration space representation for assembly tasks from demonstration
Removing suboptimal actions that can exist in a demonstration is a key problem to be solved in robot programming by demonstration. In this paper we present the first step of an approach for solving this problem. We present how the configuration space (C-space) of a task can be derived from demonstration. A demonstration traces out paths on a number of C-surfaces in C-space. The idea is to use statistical regression analysis on data from these paths to determine the unknown equation parameters of a C-surface. Experimental results show the validity of the approach. Accurate parameter estimates were obtained so long as a sufficiently rich set of demonstrated paths existed on the C-surface. The approach has the advantage that it tends to provide accurate parameter estimates for C-surfaces where they were most needed; that is, for C-surfaces (i) critical to task completion, and (ii) whose paths contained suboptimal actions.
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.
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.
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.
12493274
A Linear-Time Algorithm for Isomorphism of Graphs of Bounded Average Genus
A structure theorem is proved for the class of graphs of bounded average genus, which leads to a linear-time algorithm for isomorphism of such graphs.
8668982
Perceptually-tuned multiscale color-texture segmentation
We present a perceptually-tuned multiscale image segmentation algorithm that is based on spatially adaptive color and texture features. The proposed algorithm extends a previously proposed approach to include multiple texture scales. The determination of the multiscale texture features is based on perceptual considerations. We also examine the perceptual tuning of the algorithm and how it is affected by the presence of different texture scales. The multiscale extension is necessary for segmenting higher resolution images and is particularly effective in segmenting objects shown in different perspectives. The performance of the proposed algorithm is demonstrated in the domain of photographic images.
117657252
Labeled Search Trees and Amortized Analysis: Improved Upper Bounds for NP-Hard Problems
A sequence of exact algorithms to solve the Vertex Cover and Maximum Independent Set problems have been proposed recently in the literature. All these algorithms appeal to a very conservative analysis that considers the size of the search tree, under a worst-case scenario, to derive an upper bound on the running time of the algorithm. In this paper we propose a different approach to analyze the size of the search tree. We use amortized analysis to show how simple algorithms, if analyzed properly, may perform much better than the upper bounds on their running time derived by considering only a worst-case scenario. This approach allows us to present a simple algorithm of running time O(1.194 k + n) for the parameterized Vertex Cover problem on degree-3 graphs, and a simple algorithm of running time O(1.1254 n ) for the Maximum Independent Set problem on degree-3 graphs. Both algorithms improve the previous best algorithms for the problems.
6316461
Partitioned optimization algorithms for multiple sequence alignment
Multiple sequence alignment is an important and difficult problem in molecular biology and bioinformatics. In this paper, we propose a partitioning approach that significantly improves the solution time and quality by utilizing the locality structure of the problem. The algorithm solves the multiple sequence alignment in three stages. First, an automated and suboptimal partitioning strategy is used to divide the set of sequences into several subsections. Then a multiple sequence alignment algorithm based on ant colony optimization is used to align the sequences of each subsection. Finally, the alignment of original sequences can be obtained by assembling the result of each subsection. The ant colony algorithm is highly optimized in order to avoid local optimal traps and converge to global optimal efficiently. Experimental results show that the algorithm can significantly reduce the running time and improve the solution quality on large-scale multiple sequence alignment benchmarks.
16171801
Regular Mesh Construction Algorithms using Regular Handles
This paper presents our recent theoretical results on high genus modeling. We introduce a new concept called regular handles. Using regular handles it is possible to increase genus without increasing the number of vertices. Using regular handles a wide variety of mesh structures can be constructed. One of the usages of regular handles is to construct families of regular meshes, which is useful to create a wide variety of high genus mesh structures