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Bayesian Map Learning in Dynamic Environments
 In Neural Info. Proc. Systems (NIPS
"... We show how map learning can be formulated as inference in a graphical model, which allows us to handle changing environments in a natural manner. We describe several different approximation schemes for the problem, and illustrate some results on a simulated gridworld with doors that can open a ..."
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Cited by 136 (4 self)
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We show how map learning can be formulated as inference in a graphical model, which allows us to handle changing environments in a natural manner. We describe several different approximation schemes for the problem, and illustrate some results on a simulated gridworld with doors that can open and close. We close by briefly discussing how to learn more general models of (partially observed) environments, which can contain a variable number of objects with changing internal state. 1 Introduction Mobile robots need to navigate in dynamic environments: on a short time scale, obstacles, such as people, can appear and disappear, and on longer time scales, structural changes, such as doors opening and closing, can occur. In this paper, we consider how to create models of dynamic environments. In particular, we are interested in modeling the location of objects, which we can represent using a map. This enables the robot to perform path planning, etc. We propose a Bayesian approach in ...
Learning Models for Robot Navigation
, 1998
"... Hidden Markov models (hmms) and partially observable Markov decision processes (pomdps) provide a useful tool for modeling dynamical systems. They are particularly useful for representing environments such as road networks and office buildings, which are typical for robot navigation and planning. Th ..."
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Cited by 26 (2 self)
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Hidden Markov models (hmms) and partially observable Markov decision processes (pomdps) provide a useful tool for modeling dynamical systems. They are particularly useful for representing environments such as road networks and office buildings, which are typical for robot navigation and planning. The work presented here describes a formal framework for incorporating readily available odometric information into both the models and the algorithm that learns them. By taking advantage of such information, learning hmms/pomdps can be made better and require fewer iterations, while being robust in the face of data reduction. That is, the performance of our algorithm does not significantly deteriorate as the training sequences provided to it become significantly shorter. Formal proofs for the convergence of the algorithm to a local maximum of the likelihood function are provided. Experimental results, obtained from both simulated and real robot data, demonstrate the effectiveness of the approach....
Training Deformable Models for Localization
 In IEEE International Conference on Computer Vision and Pattern Recognition
, 2006
"... We present a new method for training deformable models. Assume that we have training images where part locations have been labeled. Typically, one fits a model by maximizing the likelihood of the part labels. Alternatively, one could fit a model such that, when the model is run on the training image ..."
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Cited by 21 (6 self)
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We present a new method for training deformable models. Assume that we have training images where part locations have been labeled. Typically, one fits a model by maximizing the likelihood of the part labels. Alternatively, one could fit a model such that, when the model is run on the training images, it finds the parts. We do this by maximizing the conditional likelihood of the training data. We formulate modellearning as parameter estimation in a conditional random field (CRF). Initializing parameters with their maximum likelihood estimates, we reach the global optimum by gradient ascent. We present a learning algorithm that searches exhaustively over all part locations in an image without relying on feature detectors. This provides millions of examples of training data, and seems to avoid overfitting issues known with CRFs. Results for part localization are relatively scarce in the community. We present results on three established datasets; Caltech motorbikes [8], USC people [19], and Weizmann horses [3]. In the Caltech set we significantly outperform the stateoftheart [6]. For the challenging people dataset, we present results that are comparable to [19], but are obtained using a significantly more generic model (devoid of a face or skin detector). Our model is general enough to find other articulated objects; we use it to recover poses of horses in the challenging Weizmann database. 1.
Directional features in online handwriting recognition
, 2006
"... The selection of valuable features is crucial in pattern recognition. In this paper we deal with the issue that part of features originate from directional instead of common linear data. Both for directional and linear data a theory for a statistical modeling exists. However, none of these theories ..."
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Cited by 14 (1 self)
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The selection of valuable features is crucial in pattern recognition. In this paper we deal with the issue that part of features originate from directional instead of common linear data. Both for directional and linear data a theory for a statistical modeling exists. However, none of these theories gives an integrated solution to problems, where linear and directional variables are to be combined in a single, multivariate probability density function. We describe a general approach for a unified statistical modeling, given the constraint that variances of the circular variables are small. The method is practically evaluated in the context of our online handwriting recognition system frog on hand and the socalled tangent slope angle feature. Recognition results are compared with two alternative modeling approaches. The proposed solution gives significant improvements in recognition accuracy, computational speed and memory requirements.
Learning GeometricallyConstrained Hidden Markov Models for Robot Navigation: Bridging the TopologicalGeometrical Gap
 Journal of AI Research
, 2002
"... Hidden Markov models (hmms) and partially observable Markov decision processes (pomdps) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and office buildings, which are typical for robot navigatio ..."
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Cited by 9 (0 self)
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Hidden Markov models (hmms) and partially observable Markov decision processes (pomdps) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and office buildings, which are typical for robot navigation and planning. The work presented here describes a formal framework for incorporating readily available odometric information and geometrical constraints into both the models and the algorithm that learns them. By taking advantage of such information, learning hmms/pomdps can be made to generate better solutions and require fewer iterations, while being robust in the face of data reduction. Experimental results, obtained from both simulated and real robot data, demonstrate the effectiveness of the approach.
Adaptive Intelligent Mobile Robots
"... As a result of this research, we will have a new architecture for mobile robotics that uses both human programming and trialanderror learning; that plans and learns hierarchically at a variety of levels of abstraction and at di#erent points in the reactive/deliberative spectrum; and that is aware ..."
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As a result of this research, we will have a new architecture for mobile robotics that uses both human programming and trialanderror learning; that plans and learns hierarchically at a variety of levels of abstraction and at di#erent points in the reactive/deliberative spectrum; and that is aware of its own knowledge and lack of knowledge and takes explicit action to gain information. This architecture will enable a revolution in mobile robot applications. 2 B Technical Rationale and Approach B.1 Introduction The theses stated above form the technical rationale for the project. In this section, we elaborate on them. 1. The necessity for learning and programming It is widely understood that there is no free lunch in machine learning. It is formally, technically impossible to predict a future you have never seen unless you have some information that constrains in advance the possible universes you operate in. This insight h