Training a policy in a source domain for deployment in the target domain under a dynamics shift can be challenging, often resulting in performance degradation.

We propose a probabilistic perspective on adversarial examples, allowing us to embed subjective understanding of semantics as a distribution into the process of generating adversarial examples, in a principled manner.

We introduce X-Ray, a novel 3D sequential representation inspired by the penetrability of x-ray scans. X-Ray transforms a 3D object into a series of surface frames atdifferent layers, making itsuitable for generating 3D models from images.