Clustering aims to find useful hidden structures in data. In this paper we present a new clustering algorithm that builds upon the consistency method (Zhou, et.al., 2003), a semi-supervised learning technique with the property of learning very smooth functions with respect to the intrinsic structure revealed by the data. Other methods, e.g. Spectral Clustering, obtain good results on data that reveals such a structure. However, unlike Spectral Clustering, our algorithm effectively detects both global and within-class outliers, and the most representative examples in each class. Furthermore, we specify an optimization framework that estimates all learning parameters, including the number of clusters, directly from data. Finally, we show that the learned cluster-models can be used to add previously unseen points to clusters without re-learning the original cluster model. Encouraging experimental results are obtained on a number of real world problems.

We derive categories directly from robot sensor data to address the symbol grounding problem. Unlike model-based approaches where human intuitive correspondences are sought between sensor readings and facets of an environment (corners, doors, etc.), our method learns intrinsic categories (or natural kinds) from the raw data itself. We approximate a manifold underlying sensor data using Isomap nonlinear dimension reduction and use Bayesian clustering (Gaussian mixture models) with model identification techniques to discover kinds. Applying our technique to sensor data of different modalities and from different physical spaces we demonstrate robustness with respect to noise and robot location. We also demonstrate a method for applying learned kinds to new sensor data (out-of-sample readings) in real time to show the efficacy of our technique as a foundation for topological mapping and autonomous control. Lastly, we discuss the application of our technique toward massive (250,000 datapoint) data sets. 1.

We propose an approach to building topological maps of environments based on image sequences. The central idea is to use manifold constraints to find representative feature prototypes, so that images can be related to each other, and thereby to camera poses in the environment. Our topological map is built incrementally, performing well after only a few visits to a location. We compare our method to several other approaches to representing images. During tests on novel images from the same environment, our method attains the highest accuracy in finding images depicting similar camera poses, including generalizing across considerable seasonal variations. 1.

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Abstract This paper addresses the problem of localization and map construction by a mobile robot in an indoor environment. Instead of trying to build high-fidelity geometric maps, we focus on constructing topological maps as they are less sensitive to poor odometry estimates and position errors. We propose a modification to the standard SLAM algorithm in which the assumption that the robots can obtain metric distance/bearing information to landmarks is relaxed. Instead, the robot registers a distinctive sensor “signature”, based on its current location, which is used to match robot positions. In our formulation of this nonlinear estimation problem, we infer implicit position measurements from an image recognition algorithm. We propose a method for incrementally building topological maps for a robot which uses a panoramic camera to obtain images at various locations along its path and uses the features it tracks in the images to update the topological map. The method is very general and does not require the environment to have uniquely distinctive features. Two algorithms are implemented to address this problem. The Iterated form of the Extended Kalman Filter (IEKF) and a batch-processed linearized ML estimator are compared under various odometric noise models.

We present an implementation and analysis of a real-time, on-line, supervised learning system for non-parametrically learning behaviors from a human trainer on a mobile robot in outdoor environments. This approach enables a human operator to train and tune robot behaviors simply by driving the robot with a remote control. Hand-designed behaviors for outdoor environments often require many parameters, and complicated behaviors can be difficult or impossible to specify with a manageable number of parameters. Furthermore, their design requires knowledge of the robot’s internal models, and knowledge of the environment in which the behaviors will be used. In real-world scenarios, we can design new behaviors using our learning system much more quickly than we can write hand-crafted behaviors. We present the results of training the robot to execute several specialized and general-purpose behaviors, including traversing a slalom, staying near “cover”, navigating on paths, navigating in an obstacle field, and general-purpose navigation. Our system learns and executes most of these behaviors well after 1-4 hours of operator training time. In quantitative tests, the learned behavior is not as robust as a hand-crafted behavior, but often completes obstacle courses more quickly. Additionally, we identify the factors that influence the effectiveness of this approach and investigate the properties of the training data provided by the human trainer. Based on our analyses, we suggest future work to ensure sufficient training, handle conflicting training examples, model robot dynamics, and further investigate dimensionality reduction of perception features. 1