Learning multidimensional joint control of a robot using receding horizon locally weighted regression
Lehnert, Christopher & Wyeth, Gordon (2011) Learning multidimensional joint control of a robot using receding horizon locally weighted regression. In Australasian Conference on Robotics and Automation (ACRA 2011), 7-9 December 2011, Monash University, Melbourne, VIC. (In Press)
In this paper we explore the ability of a recent model-based learning technique Receding Horizon Locally Weighted Regression (RH-LWR) useful for learning temporally dependent systems. In particular this paper investigates the application of RH-LWR to learn control of Multiple-input Multiple-output robot systems. RH-LWR is demonstrated through learning joint velocity and position control of a three Degree of Freedom (DoF) rigid body robot.
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|Item Type:||Conference Paper|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2011 [please consult the author]|
|Deposited On:||22 Nov 2011 08:28|
|Last Modified:||22 Nov 2011 12:00|
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