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dc.contributor.author Meng, Qinggang
dc.contributor.author Lee, Mark
dc.date.accessioned 2006-04-06T13:58:18Z
dc.date.available 2006-04-06T13:58:18Z
dc.date.issued 2004
dc.identifier.citation Meng , Q & Lee , M 2004 , ' Learning and Control in Assistive Robotics for the Elderly ' Paper presented at 2004 IEEE Conference on Robotics, Automation and Mechatronics , Singapore , 1/12/04 - 3/12/04 , pp. 71-76 . en
dc.identifier.citation conference en
dc.identifier.other PURE: 67561
dc.identifier.other dspace: 2160/105
dc.identifier.uri http://hdl.handle.net/2160/105
dc.description Q. Meng and M. H. Lee, Learning and Control in Assistive Robotics for the Elderly, IEEE Conference on Robotics, Automation and Mechatronics (RAM), Singapore, 2004. en
dc.description.abstract The worldwide population of elderly people is rapidly growing and is set to become a major problem in the coming decades. This phenomenon has the potential to create a huge market for domestic service robots that can assist with the care and support of the elderly. Robots that are able to help the user with specific physical tasks are likely to become very important in the future, but so far, unlike industrial robots, assistive robots are still under-developed and are not widely used. We analyse the nature of the requirements for assistive robotics for the elderly and argue that traditional "industrial" robot design and control approaches are inappropriate to tackle the key problem areas of safety, adaptivity, long-term autonomy of operation, user-friendliness and low costs. We present a novel approach to the control of autonomous assistive robots for the home, with emphasis on the special requirements for in situ learning, including software compensation for low precision hardware components. Our system consists of a modified behaviour-based architecture with integrated knowledge representation and planning abilities. Automatic error-recovery is implemented as an activation spreading mechanism and is distributed across the behaviour repertoire. Context-based experience is learned during both error recovery and normal action and assimilated into the behaviours. This allows reuse across different tasks, and facilitates gradual but life-long improvements in system performance. To evaluate our approach, an experimental laboratory testbed was constructed using low-cost, low-precision components. Our system was implemented in software and a series of experiments were performed in order to investigate a range of tasks. The tasks were selected to face some of the key issues identified and the results show the potential for software solutions to overcome the barriers to successful assistive robotics for the elderly. The methods, experiments and results are described in this paper. en
dc.language.iso eng
dc.relation.ispartof en
dc.title Learning and Control in Assistive Robotics for the Elderly en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Intelligent Robotics Group en
dc.description.status Non peer reviewed en


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