R. Te Boekhorst

Effects of repeated exposure to a humanoid robot on children with autism

This work is part of the Aurora project which investigates the possible use of robots in therapy and education of children with autism (Aurora, 2003), based on findings that people with autism enjoy interacting with computers, e.g. (Powell, 1996). In most of our trials we have been using mobile robots, e.g. (Dautenhahn and Werry, 2002). More recently we tested the use of a humanoid robotic doll. In (Dautenhahn and Billard, 2002) we reported on a first set of trials with 14 autistic subjects interacting with this doll. In this chapter we discuss lessons learnt from our previous study, and introduce a new approach, heavily inspired by therapeutic issues. A longitudinal study with four children with autism is presented. The children were repeatedly exposed to the humanoid robot over a period of several months. Our aim was to encourage imitation and social interaction skills. Different behavioural criteria (including Eye Gaze, Touch, and Imitation) were evaluated based on the video data of the interactions. The chapter exemplifies the results that clearly demonstrate the crucial need for long-term studies in order to reveal the full potential of robots in therapy and education of children with autism.

Human approach distances to a mechanical-looking robot with different robot voice styles

Findings are presented from a Human Robot Interaction (HRI) Demonstration Trial where attendees approached a stationary mechanical looking robot to a comfortable distance. Instructions were given to participants by the robot using either a high quality male, a high quality female, a neutral synthesized voice, or by the experimenter (no robot voice). Approaches to the robot with synthesized voice were found to induce significantly further approach distances. Those who had experienced a previous encounter with the robot tended to approach closer to the robot. Possible reasons for this are discussed.

Exploratory studies on social spaces between humans and a mechanical-looking robot

The results from two empirical studies of human–robot interaction are presented. The first study involved the subject approaching the static robot and the robot approaching the standing subject. In these trials a small majority of subjects preferred a distance corresponding to the ‘personal zone’ typically used by humans when talking to friends. However, a large minority of subjects got significantly closer, suggesting that they treated the robot differently from a person, and possibly did not view the robot as a social being. The second study involved a scenario where the robot fetched an object that the seated subject had requested, arriving from different approach directions. The results of this second trial indicated that most subjects disliked a frontal approach. Most subjects preferred to be approached from either the left or right side, with a small overall preference for a right approach by the robot. Implications for future work are discussed.

Using Feature Selection Filtering Methods for Binding Site Predictions

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we applied classification techniques on predictions from 12 key prediction algorithms. In this paper, we investigate the classification results when 4 feature selection filtering methods are used. They are bi-normal separation, correlation coefficients, F-score and a cross entropy based algorithm. It is found that all 4 filtering methods perform equally well. Moreover, we show that the worst performing algorithms are not detrimental to the overall performance

Pattern Formation in Homogeneous and Heterogeneous Swarms: Differences Between Versatile and Specialized Agents

In collective robotics, researchers are successfully using models derived from swarm insect behavior to solve problems like coordination or task allocation. It is often assumed that in a homogeneous group of agents, every agent has to become more complicated when the complexity of the task increases, which decreases simplicity of design and robustness. Role diversification and therefore task specialization within a group may help counter the need for more complex agents. Because experiments on self-organization and dynamic task allocation in robot populations focused mainly on homogeneous groups, the relation between these models and pre-specified role diversification remains mainly unknown. In this work the interchangeability of homogeneous and heterogeneous agent populations is investigated. It is shown that in a simple simulated environment, a mixed population of specialized agents can not be easily substituted by a homogeneous group of multi-tasking agents. Results lead to the conclusion that the ability of dynamic task switching, i.e. adaptive task allocation in respect to changes in the environment, have strong effects on the behavior on a population level. Although a pre-defined heterogeneous group can produce the same result for a given environment and a specific population composition, the group behavior differs when the environment changes

Developing social action capabilities in a humanoid robot using an interaction history architecture

We present experimental results for the humanoid robot Kaspar2 engaging in a simple ldquopeekaboordquo interaction game with a human partner. The robot develops the capability to engage in the game by using its history of interactions coupled with audio and visual feedback from the interaction partner to continually generate increasingly appropriate behaviour. The robot also uses facial expressions to feedback its level of reward to the partner. The results support the hypothesis that reinforcement of time-extended experiences through interaction allows a robot to act appropriately in an interaction.

New Methods to Infer DNA Function from Sequence Information

We present a new computational approach to infer DNA function from eukaryotic DNA sequence information. It is based on the fact that exons, regulatory regions, and non-coding non-regulatory DNA exhibit different statistical patterns. We suggest capturing and measuring these patterns by the following suite of statistical tools: (1) the ‘fluffy-tail’ test, a bootstrap procedure to recognize statistically significant abundant similar words in regulatory DNA; (2) an algorithm to assess the density of patches of low entropy as a new measure of homogeneity. This measure can be used to distinguish coding from non-coding and regulatory regions; (3) an adaptive window technique applied to rescaled range analysis and entropy measurements. This is an optimization technique to segment DNA into homogeneous parts (that are therefore likely to be coding), of which the outcomes are independent of the size of the sliding window and hence avoids averaging. The application of our methods to several annotated data sets from six eukaryotic species enables a clear separation of coding, regulatory, and non-coding non-regulatory DNA. We propose that established computational methods complemented by our new statistical tests and augmented with the novel optimization technique for sliding windows create a powerful tool for the characterization and annotation of DNA sequences. The software is available from the authors on request.