  We propose an architecture for training generative models of counterfactual
conditionals of the form, 'can we modify event A to cause B instead of C?',
motivated by applications in robot control. Using an 'adversarial training'
paradigm, an image-based deep neural network model is trained to produce small
and realistic modifications to an original image in order to cause user-defined
effects. These modifications can be used in the design process of image-based
robust control - to determine the ability of the controller to return to a
working regime by modifications in the input space, rather than by adaptation.
In contrast to conventional control design approaches, where robustness is
quantified in terms of the ability to reject noise, we explore the space of
counterfactuals that might cause a certain requirement to be violated, thus
proposing an alternative model that might be more expressive in certain
robotics applications. So, we propose the generation of counterfactuals as an
approach to explanation of black-box models and the envisioning of potential
movement paths in autonomous robotic control. Firstly, we demonstrate this
approach in a set of classification tasks, using the well known MNIST and
CelebFaces Attributes datasets. Then, addressing multi-dimensional regression,
we demonstrate our approach in a reaching task with a physical robot, and in a
navigation task with a robot in a digital twin simulation.
