Adaptive Visual Face Tracking for an Autonomous Robot

Perception is an essential ability for autonomous robots in non-standardized conditions. However, the appearance of objects can change between different conditions. A system visually tracking a target based on its appearance could lose its target in those cases. A tracker learning the appearance of the target in different conditions should perform better at this task. To learn reliably, the system needs feedback. In this study, feedback is provided by a secondary teacher system that trains the tracker. The learning tracker is compared to a baseline non-learning tracker using data from an autonomous mobile robot operating in realistic conditions. In this experiment, the learning tracker outperforms the non-learning tracker. This shows that we successfully used the teacher system to train a visual tracking system on an autonomous robot

RoboCup@Home: Analysis and results of evolving competitions for domestic and service robots

Scientific competitions are becoming more common in many research areas of artificial intelligence and robotics, since they provide a shared testbed for comparing different solutions and enable the exchange of research results. Moreover, they are interesting for general audiences and industries. Currently, many major research areas in artificial intelligence and robotics are organizing multiple-year competitions that are typically associated with scientific conferences. One important aspect of such competitions is that they are organized for many years. This introduces a temporal evolution that is interesting to analyze. However, the problem of evaluating a competition over many years remains unaddressed. We believe that this issue is critical to properly fuel changes over the years and measure the results of these decisions. Therefore, this article focuses on the analysis and the results of evolving competitions. In this article, we present the RoboCup@Home competition, which is the largest worldwide competition for domestic service robots, and evaluate its progress over the past seven years. We show how the definition of a proper scoring system allows for desired functionalities to be related to tasks and how the resulting analysis fuels subsequent changes to achieve general and robust solutions implemented by the teams. Our results show not only the steadily increasing complexity of the tasks that RoboCup@Home robots can solve but also the increased performance for all of the functionalities addressed in the competition. We believe that the methodology used in RoboCup@Home for evaluating competition advances and for stimulating changes can be applied and extended to other robotic competitions as well as to multi-year research projects involving Artificial Intelligence and Robotics

Handwritten-word spotting using biologically inspired features

For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language, and collection. We propose a biologically inspired whole-word recognition method that is used to incrementally elicit word labels in a live Web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neurophysiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows us to classify text images that have a low frequency of occurrence. Typically, these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually, standard pattern-recognition technology cannot deal with these text images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising

Benchmarking intelligent service robots through scientific competitions: The RoboCup@Home approach

The dynamical and uncertain environments of domestic service robots, which includehumans, require rethinking of the benchmarking principles for testing these robots. Since 2006 RoboCup@Home has used statistical procedures to track and steer the progress ofdomestic service robots. This paper explains the procedures and shows outcomes of these international benchmarking efforts. Although aspects such as shopping in a supermarket receive a fair amount of attention in the robotics community, the authors believe that a recently implemented test is the most important outcome of RoboCup@Home, namely the benchmarking of robot cognition. © 2013, Association for the Advancement of artificial intelligence