{"title":"Acquiring manipulation skills through observation","authors":"H. Tominaga, K. Ikuuchi","doi":"10.1109/MFI.1999.815956","DOIUrl":"https://doi.org/10.1109/MFI.1999.815956","url":null,"abstract":"Currently, most robot programming is done either by manual programming or using a teach pendant as part of the \"teach-by-showing\" method. Both of these methods have been found to have several drawbacks. To solve the problems, the assembly-plan-from-observation (APO) method was proposed, which has the capability of observing a human performing an assembly task, understanding the task, and subsequently generating a robot program to achieve the same task. This system, however, cannot observe a trajectory of a human performance. Necessary trajectories are generated from CAD models. Later, in order to overcome this problem, a direct observation method based on a trajectory of a human performance was proposed to project human trajectory to robot trajectory. Though its implementation is relatively easy, the system is susceptive against observation noise. This paper proposes a method to make the robust observation against noise using symbolic representations such as face contact transitions. The system divides the trajectory into small segments based on the face contact analysis, allocates an operation element, referred to as sub-skill, to those segments. By using this system, we can decompose large motion templates, employed in the previous system, into sets of smaller sub-skills.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131517334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Projective method for generic sensor fusion problem","authors":"N. Rao","doi":"10.1109/MFI.1999.815955","DOIUrl":"https://doi.org/10.1109/MFI.1999.815955","url":null,"abstract":"In a multiple sensor system, each sensor produces an output which is related to the desired feature according to a certain probability distribution. We propose a fuser that combines the sensor outputs to more accurately predict the desired feature. The fuser utilizes the lower envelope of regression curves of sensors to project the sensor with the least error at each point of the feature space. This fuser is optimal among all projective fusers and also satisfies the isolation property that ensures a performance at least as good as the best sensor. In the case the sensor distributions are not known, we show that a consistent estimator of this fuser can be computed entirely based on a training sample. Compared to linear fusers, the projective fusers provide a complementary performance. We propose two classes of metafusers that utilize both linear and projective fusers to perform at least as good as the best sensor as well as the best fuser.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132895827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sensor space discretization in autonomous agent based on entropy minimization of behavior outcomes","authors":"T. Yairi, K. Hori, S. Nakasuka","doi":"10.1109/MFI.1999.815974","DOIUrl":"https://doi.org/10.1109/MFI.1999.815974","url":null,"abstract":"Sensor space discretization is a significant issue for the realization of the autonomous agents which are expected to decide and learn the proper behavior with various kinds of sensor information. This paper proposes a new sensor space discretization method based on entropy minimization of the agent's behavior outcomes. This framework unifies a variety of heuristic discretization policies used in the previous works, and provides a more general insight into this problem. An experimental study is also presented in the latter part, which suggests that our sensor discretization method greatly increases the adaptability of the agents to the environment when combined with existing behavior learning methods such as Q-Learning.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133342918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Experiments in the control of perception in multi-sensor system","authors":"T. Celinski, B. McCarragher","doi":"10.1109/MFI.1999.815959","DOIUrl":"https://doi.org/10.1109/MFI.1999.815959","url":null,"abstract":"The paper presents a new approach to management of multiple sensors and perception algorithms in a multi-sensor robotic system. The approach involves real-time selection of process monitors by a Sensory Perception Controller. The selection is based on the minimization of the expected cost of perception with constraints on the uncertainty of perception. The effectiveness and usefulness of the approach is evaluated through experiments involving a range of sensing modalities which may typically be encountered in robotic applications.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124015131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improving the efficiency of motion planning on a polyhedron","authors":"P. Cheng, Der Chin Liu","doi":"10.1109/MFI.1999.815963","DOIUrl":"https://doi.org/10.1109/MFI.1999.815963","url":null,"abstract":"This study presents a novel algorithm to derive an efficient motion planning on a polyhedron model. The proposed algorithm combines the computational geometry method with the numerical analysis method to obtain the bend points that pass through the shortest path. This algorithm should improve the efficiency of the motion planning process. The computational geometry method is applied to create fractional patches, to retrieve information about the patches, and to calculate the distance between two points. The numerical analysis method is applied to derive the feasible paths, and then the Djikstra method is used to identify the shortest path between specific points of the edge boundary. An illustrative example demonstrates this algorithm's feasibility and effectiveness. The purpose of this research is to simplify the process of variant motion planning, eliminate the time needed for estimating the optimal solution of the Voronoi method, and enhance the working efficiency.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126920549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The two eyes position detection algorithm for a moving viewer using color camera image","authors":"K. Huh","doi":"10.1109/MFI.1999.815988","DOIUrl":"https://doi.org/10.1109/MFI.1999.815988","url":null,"abstract":"In this paper, we suggest methods of detecting the two-eyes position of moving viewer by using images obtained through the color CCD camera. This procedure is necessary for future 3D display technique, in which the moving viewer can see the 3D image continuously. We develop two algorithms for detecting the position of two eyes: one is the method of using this mask block and the other is that of using pixel analysis.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133879404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Control of foveated vision based on short-term memory","authors":"S. Morita, Y. Ishihara","doi":"10.1109/MFI.1999.815958","DOIUrl":"https://doi.org/10.1109/MFI.1999.815958","url":null,"abstract":"We propose an active foveated vision system based on both the sensory register and the short term memory. The eye has two states of movement. The first, saccard movement, is when the eye moves in the wide region and this was actualized by utilizing a multiple image short-term memory which could save some images. The second state, attention, involves movement in the interest region and this state was achieved using a single image short-term memory which could save only a single image. The total eye movement was then realized by selecting two states. The movement of eye is defined by the image of image features generated by low level vision. We generate the image that we imagine that we see at any instant in time on short-term memory.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130166708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Distributed multiple view fusion for two-arm distance estimation","authors":"C. Scheering, J. Zhang, A. Knoll","doi":"10.1109/MFI.1999.815962","DOIUrl":"https://doi.org/10.1109/MFI.1999.815962","url":null,"abstract":"We propose an approach for estimating the distance of two moving robot arms based on fusion of multiple vision data. The images are fused by simple concatenation of all images followed by a projection of the high-dimensional visual input data into an appropriate low-dimensional subspace. We extended the well known principal component analysis to the so-called output relevant features and present a distributed online computation algorithm performing the projection in parallel. We show that complex sensor data can be efficiently compressed if the robot motions are constrained to a local scenario. The second component of our model is an adaptive B-spline neuro-fuzzy controller whose input space is the constructed subspace and whose output is the estimated distance. Our experimental setup is a two-arm robot system with four uncalibrated cameras. Experiments with a complex circular motion show that the method works even if no robust geometric features can be extracted from the sensor pattern.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129747277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Vision feature fusion for classification of electrical motors in an industrial recycling process","authors":"B. Karlsson, J. Jarrhed, P. Wide","doi":"10.1109/MFI.1999.815970","DOIUrl":"https://doi.org/10.1109/MFI.1999.815970","url":null,"abstract":"This paper describes the fusion process of image features from vision data in an industrial system for disassembly of worn out electrical motors. In the fusion process fuzzy measures are used to fuse Gaussian shaped possibility measures. After training, the system is able to correctly classify incoming motors to a degree of 95%.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117181827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Kobayashi, F. Arai, T. Fukuda, M. Onoda, Y. Hotta
{"title":"Sensor selected fusion system (application to interference of surface roughness in grinding system)","authors":"F. Kobayashi, F. Arai, T. Fukuda, M. Onoda, Y. Hotta","doi":"10.1109/MFI.1999.815976","DOIUrl":"https://doi.org/10.1109/MFI.1999.815976","url":null,"abstract":"In grinding process, sensor fusion methods for inferring the surface roughness from online sensing information have received much attention, because it is quite time consuming to measure manually the surface roughness during process. In addition, recently, it is necessary to select the sensing information for adapting to various situation flexibly. This paper proposes a sensor fusion system with sensor selection method for a grinding system. The sensor selection method is based on the production rules, considering the reliability calculated by the possibility measure which is a type of fuzzy measure. Then, the sensor fusion system fuses the sensor information selected by the sensor selection method using a recurrent neural network.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120966568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}