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Assistive mobile manipulators have the potential to one day serve as surrogates and helpers for people with disabilities, giving them the freedom to perform tasks such as scratching an itch, picking up a cup, or socializing with their families. This article introduces a collaborative project with the goal of putting assistive mobile manipulators into real homes to work with people with disabilities. Through a participatory design process in which users have been actively involved from day one, we are identifying and developing assistive capabilities for the PR2 robot. Our approach is to develop a diverse suite of open source software tools that blend the capabilities of the user and the robot. Within this article, we introduce the project, describe our progress, and discuss lessons we have learned.

Similarly to adult brain signals, pediatric brain signals can be decoded and used for BCI operation. Therefore, BCI systems developed for adults likely hold similar promise for children with motor disabilities.

We describe an approach to the use of genetic programming for multi-class object classification problems. Rather than using fixed static thresholds as boundaries to distinguish between different classes, this approach introduces two methods of classification where the boundaries between different classes can be dynamically determined during the evolutionary process. The two methods are centred dynamic class boundary determination and slotted dynamic class boundary determination. The two methods are tested on four object classification problems of increasing difficulty and are compared with the commonly used static class boundary method. The results suggest that, while the static class boundary method works well on relatively easy object classification problems, the two dynamic class boundary determination methods outperform the static method for more difficult, multiple class object classification problems.Keywords Genetic programming, genetic algorithms, dynamic class boundary determination, object recognition. Author InformationMengjie Zhang is an academic staff member in computer science and Will Smart is a postgraduate student in computer science. Both authors are in the School of Mathematical and Computing Sciences, Victoria University of Wellington, New Zealand. Multiclass Object Classification Using Genetic Programming Mengjie Zhang and Will SmartSchool of Mathematical and Computing Sciences Victoria University of Wellington, P. O. Box 600, Wellington, New Zealand, Email: {mengjie,willsmart}@mcs.vuw.ac.nz Abstract. We describe an approach to the use of genetic programming for multi-class object classification problems. Rather than using fixed static thresholds as boundaries to distinguish between different classes, this approach introduces two methods of classification where the boundaries between different classes can be dynamically determined during the evolutionary process. The two methods are centred dynamic class boundary determination and slotted dynamic class boundary determination. The two methods are tested on four object classification problems of increasing difficulty and are compared with the commonly used static class boundary method. The results suggest that, while the static class boundary method works well on relatively easy, linearly separable object classification problems, the two dynamic class boundary determination methods outperform the static method for more difficult, multiple class object classification problems.