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

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/ICHR.2008.4756013We present experimental results for the humanoid robot Kaspar2 engaging in a simple “peekaboo” 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

Robot Self-Characterisation of Experience Using Trajectories in Sensory-Motor Phase Space

Peer reviewe

Exploring the Design Space of Robot Appearance and Behavior in an Attention-Seeking Living Room Scenario for a Robot Companion

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. --DOI : 10.1109/ALIFE.2007.36781

A middleware for a large array of cameras

Large arrays of cameras are increasingly being employed for producing high quality image sequences needed for motion analysis research. This leads to the logistical problem with coordination and control of a large number of cameras. In this paper, we used a lightweight multi-agent system for coordinating such camera arrays. The agent framework provides more than a remote sensor access API. It allows reconfigurable and transparent access to cameras, as well as software agents capable of intelligent processing. Furthermore, it eases maintenance by encouraging code reuse. Additionally, our agent system includes an automatic discovery mechanism at startup, and multiple language bindings. Performance tests showed the lightweight nature of the framework while validating its correctness and scalability. Two different camera agents were implemented to provide access to a large array of distributed cameras. Correct operation of these camera agents was confirmed via several image processing agents

An empirical framework for human-robot proxemics

The work described in this paper was conducted within the EU Integrated Projects COGNIRON ("The Cognitive Robot Companion") and LIREC (LIving with Robots and intEractive Companions) and was funded by the European Commission under contract numbers FP6- 002020 and FP7-215554.An empirical framework for Human-Robot (HR) proxemics is proposed which shows how the measurement and control of interpersonal distances between a human and a robot can be potentially used by the robot to interpret, predict and manipulate proxemic behaviour for Human-Robot Interactions (HRIs). The proxemic framework provides for incorporation of inter-factor effects, and can be extended to incorporate new factors, updated values and results. The framework is critically discussed and future work proposed

Some statistical properties of regulatory DNA sequences, and their use in predicting regulatory regions in the Drosophila genome: the fluffy-tail test.

BACKGROUND: This paper addresses the problem of recognising DNA cis-regulatory modules which are located far from genes. Experimental procedures for this are slow and costly, and computational methods are hard, because they lack positional information. RESULTS: We present a novel statistical method, the "fluffy-tail test", to recognise regulatory DNA. We exploit one of the basic informational properties of regulatory DNA: abundance of over-represented transcription factor binding site (TFBS) motifs, although we do not look for specific TFBS motifs, per se . Though overrepresentation of TFBS motifs in regulatory DNA has been intensively exploited by many algorithms, it is still a difficult problem to distinguish regulatory from other genomic DNA. CONCLUSION: We show that, in the data used, our method is able to distinguish cis-regulatory modules by exploiting statistical differences between the probability distributions of similar words in regulatory and other DNA. The potential application of our method includes annotation of new genomic sequences and motif discovery.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Close encounters : spatial distances between people and a robot of mechanistic appearance

Peer reviewe

Robotic assistants in therapy and education of children with autism: Can a small humanoid robot help encourage social interaction skills?

This article presents a longitudinal study with four children with autism, who were exposed to a humanoid robot over a period of several months. The longitudinal approach allowed the children time to explore the space of robot– human, as well as human–human interaction. Based on the video material documenting the interactions, a quantitative and qualitative analysis was conducted. The quantitative analysis showed an increase in duration of pre-defined behaviours towards the later trials. A qualitative analysis of the video data, observing the children’s activities in their interactional context, revealed further aspects of social interaction skills (imitation, turn-taking and role- switch) and communicative competence that the children showed. The results clearly demonstrate the need for, and benefits of, long-term studies in order to reveal the full potential of robots in the therapy and education of children with autism

Effect of repeated exposure of a humanoid robot on children with autism - Can we encourage basic social interaction skills

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 paper 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 paper exemplifies the results that clearly demo nstrate the crucial need for long-term studies in order to reveal the full potential of robots in therapy and education of children with autism