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Learning to Control in Operational Space
2008
The international journal of robotics research
One of the most general frameworks for phrasing control problems for complex, redundant robots is operational space control. However, while this framework is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in complex robots, e.g., humanoid robots. In this paper, we suggest a learning approach for opertional space control as a direct inverse model
doi:10.1177/0278364907087548
fatcat:fpif4lt4pvgjdmry2rtbhxwk5e