--- layout: default --- Publication details Learning to Coordinate Behaviours on a Four-Legged Robot Tim Taylor 1993 Abstract An algorithm has been developed elsewhere which enabled a six-legged robot to learn how to coordinate its leg movements and walk with a statically stable gait. In the current project, the applicability of this algorithm to a four-legged robot was investigated. A physical machine was constructed, along with a computer simulation. There was insufficient time between the completion of the robot and producing this report to test the algorithm on the physical machine, so the results described apply to the simulation only. The basic finding was that the robot could not learn how to coordinate its legs so that it never fell over. Several variations and extensions of the basic algorithm were also investigated, some of which resulted in a marked improvement in performance so that, in some cases, the robot would only fall occasionally. To model the real robot more closely, some trials on the simulation included a degree of noise in the leg movements. This was found to have a (sometimes drastically) detrimental effect on the level of performance achieved. Some reasons for the failure of the algorithm to satisfactorily translate from a six legged robot to a four-legged robot are discussed, and possible extensions, which may lead to improved performance, are suggested. Full text Author preprint: pdf Reference Taylor, T. (1993). Learning to Coordinate Behaviours on a Four-Legged Robot (Master's thesis). Department of Artificial Intelligence, University of Edinburgh. BibTeX @mastersthesis{taylor1993learning, author = {Taylor, Tim}, title = {Learning to Coordinate Behaviours on a Four-Legged Robot}, school = {Department of Artificial Intelligence, University of Edinburgh}, year = {1993}, category = {dissertation}, keywords = {robots} } Related publications
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  3. Østergaard, E. H., Christensen, D. J., Eggenberger, P., Taylor, T., Ottery, P., & Lund, H. H. (2005). HYDRA: From Cellular Biology to Shape-Changing Artefacts. In W. Duch, J. Kacprzyk, E. Oja, & S. Zadrożny (Eds.), Artificial Neural Networks: Biological Inspirations – ICANN 2005 (pp. 275–281). https://doi.org/10.1007/11550822_44
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