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Over the last years, significant advances have been made in roboticmanipulation, but still, the handling of non-rigid objects, such as clothgarments, is an open problem. Physical interaction with non-rigid objects isuncertain and complex to model. Thus, extracting useful information from sampledata can considerably improve modeling performance. However, the training ofsuch models is a challenging task due to the high-dimensionality of the staterepresentation. In this paper, we propose Controlled Gaussian Process DynamicalModel (CGPDM) for learning high-dimensional, nonlinear dynamics by embedding itin a low-dimensional manifold. A CGPDM is constituted by a low-dimensionallatent space, with an associated dynamics where external control variables canact and a mapping to the observation space. The parameters of both maps aremarginalized out by considering Gaussian Process (GP) priors. Hence, a CGPDMprojects a high-dimensional state space into a smaller dimension latent space,in which it is feasible to learn the system dynamics from training data. Themodeling capacity of CGPDM has been tested in both a simulated and a realscenario, where it proved to be capable of generalizing over a wide range ofmovements and confidently predicting the cloth motions obtained by previouslyunseen sequences of control actions.

Controlled Gaussian Process Dynamical Models with Application to Robotic Cloth Manipulation