On-line learning of temporal state models for flexible objects

State estimation and control are intimately related processes in robot handling of flexible and articulated objects. While for rigid objects, we can generate a CAD model beforehand and a state estimation boils down to estimation of pose or velocity of the object, in case of flexible and articulated objects, such as a cloth, the representation of the object's state is heavily dependent on the task and execution. For example, when folding a cloth, the representation will mainly depend on the way the folding is executed. In this paper, we address the problem of learning a temporal object model from observations generated during task execution. We use the case of dynamic cloth folding as a proof-of-concept for our methodology. In cloth folding, the most important information is contained in the temporal structure of the data requiring appropriate representation of the observations, fast state estimation and a suitable prediction mechanism. Our approach is realized through efficient implementation of feature extraction and a generative process model, exploiting recent hardware advances in conjunction with principled probabilistic models. The model is capable of representing the temporal structure of the data and it is robust to noise in the observations. We present results exploiting our model to classify the success of a folding action.