Control Model Learning for Whole-Body Mobile Manipulation
DOI:
https://doi.org/10.1609/aaai.v24i1.7775Keywords:
whole-body control, mobile robotics, motor learning, L1 regularizationAbstract
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.
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Published
2010-07-05
How to Cite
Kuindersma, S. (2010). Control Model Learning for Whole-Body Mobile Manipulation. Proceedings of the AAAI Conference on Artificial Intelligence, 24(1), 1939-1940. https://doi.org/10.1609/aaai.v24i1.7775
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