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Please use this identifier to cite or link to this item: http://hdl.handle.net/1842/3702

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Title: Learning Utility Surfaces for Movement Selection
Authors: Howard, Matthew
Gienger, Michael
Goerick, Christian
Vijayakumar, Sethu
Issue Date: 2006
Journal Title: Proc. IEEE International Conference on Robotics and Biomimetics (ROBIO)
Abstract: Humanoid robots are highly redundant systems with respect to the tasks they are asked to perform. This redundancy manifests itself in the number of degrees of freedom of the robot exceeding the dimensionality of the task. Traditionally this redundancy has been utilised through optimal control in the null-space. Some cost function is defined that encodes secondary movement goals and movements are optimised with respect to this function
Keywords: Redundancy
URI: http://hdl.handle.net/1842/3702
Appears in Collections:Informatics Publications

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