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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 before-hand 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...
An important challenge in robotic research is learning and reasoning about different manipulation tasks from scene observations. In this paper we present a probabilistic model capable of modeling several different types of input sources within the same model. Our model is capable to infer the task using only partial observations. Further, our framework allows the robot, given partial knowledge of...
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