Locally weighted learning methods for non-rigid robot control

Lehnert, Christopher Francis (2015) Locally weighted learning methods for non-rigid robot control. PhD thesis, Queensland University of Technology.


This thesis develops a novel approach to robot control that learns to account for a robot's dynamic complexities while executing various control tasks using inspiration from biological sensorimotor control and machine learning. A robot that can learn its own control system can account for complex situations and adapt to changes in control conditions to maximise its performance and reliability in the real world. This research has developed two novel learning methods, with the aim of solving issues with learning control of non-rigid robots that incorporate additional dynamic complexities. The new learning control system was evaluated on a real three degree-of-freedom elastic joint robot arm with a number of experiments: initially validating the learning method and testing its ability to generalise to new tasks, then evaluating the system during a learning control task requiring continuous online model adaptation.

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47 since deposited on 16 Apr 2015
24 in the past twelve months

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ID Code: 82358
Item Type: QUT Thesis (PhD)
Supervisor: Wyeth, Gordon & Corke, Peter
Keywords: learning robot control, learning, model predictive control, elastic joint robot, artificial intelligence, learning control, locally weighted learning, locally weighted regression, robot dynamics
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 16 Apr 2015 04:56
Last Modified: 08 Sep 2015 06:21

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