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Journal Article

Model learning for robot control: a survey


Nguyen-Tuong,  D.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;


Peters,  J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Nguyen-Tuong, D., & Peters, J. (2011). Model learning for robot control: a survey. Cognitive Processing, 12(4), 319-340. doi:10.1007/s10339-011-0404-1.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-4D24-D
Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.