Abstract. We present a method of constructive induction aimed at learning tasks involving multivariate time series data. Using metafeatures, the scope of attribute-value learning is expanded to domains with instances that have some kind of recurring substructure, such as strokes in handwriting recognition, or local maxima in time series data. The types of substructures are defined by the user, but are extracted automatically and are used to construct attributes.Metafeatures are applied to two real domains: sign language recognition and ECG classification. Using metafeatures we are able to generate classifiers that are either comprehensible or accurate, producing results that are comparable to hand-crafted preprocessing and comparable to human experts.

Until robots are able to autonomously navigate, carry out a mission and report back to base, effective human-robot interfaces will be an integral part of any practical mobile robot system. This is especially the case for robot-assisted Urban Search and Rescue (USAR). Unfamiliar and unstructured environments, unreliable communications and many sensors combine to make the job of a human operator, and hence the interface designer challenging.This paper presents the design, implementation and deployment of a human-robot interface for the teleoperated USAR research robot, CASTER. Proven HCI-based user interface design principles were adopted in order to produce an interface that was intuitive and minimised learning time while maximising effectiveness.The human-robot interface was deployed by Team CASualty in the 2005 RoboCup Rescue Robot League competition. This competition allows a wide variety of approaches to USAR research to be evaluated in a realistic environment. Despite the operator having less than one month of experience, Team CASualty came 3rd, beating teams that had far longer to train their operators. In particular, the ease with which the robot could be driven and high quality information gathered played a crucial part in Team CASualty's success. Further empirical evaluations of the system on a group of twelve users as well as members of the public further reinforce our belief that this interface is quick to learn, easy to use and effective.

-One of the challenges of rescue robotics is to create robots that can autonomously traverse rough, unstructured terrain. Although mechanical engineering can produce very capable robots, mechanical engineering alone will not drive them. In this paper, we present a terrain feature extractor that can be taught to find significant features in range images of terrain around a robot from a human expert. This novel approach has the advantage that it potentially allows the human expert's knowledge to be captured rapidly. A terrain model is generated from the many points in the range sensor data. Techniques from the field of knowledge acquisition are then used to find patterns in the terrain model. A knowledge acquisition system can then be taught to drive a robot in unstructured terrain based on these features. We evaluate the performance of the initial stages of the feature extractor on a real robot, traversing NIST specification red stepfields.

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Summary. This paper presents a supervised learning algorithm for image feature matching. The algorithm is based on Conditional Random Fields which provides a mechanism for globally reason about the associations. The novelty of this work is twofold: (i) the use of Delaunay triangulation as the graph structure for a probabilistic network to reason about image feature association; (ii) the combination of local and joint features to enforce consistency in a theoretically sound statistical learning procedure. Experimental results show that our approach outperforms RANSAC in our challenging datasets consisting of indoor and outdoor images, with significant occlusion, blurring, rotational and translational transformations.