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We propose an architecture for training generative models of counterfactualconditionals 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 smalland realistic modifications to an original image in order to cause user-definedeffects. These modifications can be used in the design process of image-basedrobust control - to determine the ability of the controller to return to aworking regime by modifications in the input space, rather than by adaptation.In contrast to conventional control design approaches, where robustness isquantified in terms of the ability to reject noise, we explore the space ofcounterfactuals that might cause a certain requirement to be violated, thusproposing an alternative model that might be more expressive in certainrobotics applications. So, we propose the generation of counterfactuals as anapproach to explanation of black-box models and the envisioning of potentialmovement paths in autonomous robotic control. Firstly, we demonstrate thisapproach in a set of classification tasks, using the well known MNIST andCelebFaces Attributes datasets. Then, addressing multi-dimensional regression,we demonstrate our approach in a reaching task with a physical robot, and in anavigation task with a robot in a digital twin simulation.

Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control