  We propose a method that efficiently learns distributions over articulation
model parameters directly from depth images without the need to know
articulation model categories a priori. By contrast, existing methods that
learn articulation models from raw observations typically only predict point
estimates of the model parameters, which are insufficient to guarantee the safe
manipulation of articulated objects. Our core contributions include a novel
representation for distributions over rigid body transformations and
articulation model parameters based on screw theory, von Mises-Fisher
distributions, and Stiefel manifolds. Combining these concepts allows for an
efficient, mathematically sound representation that implicitly satisfies the
constraints that rigid body transformations and articulations must adhere to.
Leveraging this representation, we introduce a novel deep learning based
approach, DUST-net, that performs category-independent articulation model
estimation while also providing model uncertainties. We evaluate our approach
on several benchmarking datasets and real-world objects and compare its
performance with two current state-of-the-art methods. Our results demonstrate
that DUST-net can successfully learn distributions over articulation models for
novel objects across articulation model categories, which generate point
estimates with better accuracy than state-of-the-art methods and effectively
capture the uncertainty over predicted model parameters due to noisy inputs.
Project webpage: https://pearl-utexas.github.io/DUST-net/
