AAAI Publications, Twenty-Fourth AAAI Conference on Artificial Intelligence

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Control Model Learning for Whole-Body Mobile Manipulation
Scott Kuindersma

Last modified: 2010-07-05


The ability to discover the effects of actions and apply this knowledge during goal-oriented action selection is a fundamental requirement of embodied intelligent agents. In our ongoing work, we hope to demonstrate the utility of learned control models for whole-body mobile manipulation. In this short paper we discuss preliminary work on learning a forward model of the dynamics of a balancing robot exploring simple arm movements. This model is then used to construct whole-body control strategies for regulating state variables using arm motion.


whole-body control; mobile robotics; motor learning; L1 regularization

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