PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Load estimation and control using learned dynamics models
Giorgos Petkos and Sethu Vijayakumar
In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '07), 29 Oct - 2 Nov, 2007, San Diego, USA.


Classic adaptive control methods for handling varying loads rely on an analytically derived model of the robot’s dynamics. However, in many situations, it is not feasible or easy to obtain an accurate analytic model of the robot’s dynamics. An alternative to analytically deriving the dynamics is learning the dynamics from movement data. This paper describes a load estimation technique that uses the learned instead of analytically derived dynamics. We study examples where the various inertial parameters of the load are estimated from the learned models, their effectiveness in control is evaluated along with their robustness in light of imperfect, intermediate dynamic models.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3694
Deposited By:Sethu Vijayakumar
Deposited On:14 February 2008