  The Robotics community has started to heavily rely on increasingly realistic
3D simulators for large-scale training of robots on massive amounts of data.
But once robots are deployed in the real world, the simulation gap, as well as
changes in the real world (e.g. lights, objects displacements) lead to errors.
In this paper, we introduce Sim2RealViz, a visual analytics tool to assist
experts in understanding and reducing this gap for robot ego-pose estimation
tasks, i.e. the estimation of a robot's position using trained models.
Sim2RealViz displays details of a given model and the performance of its
instances in both simulation and real-world. Experts can identify environment
differences that impact model predictions at a given location and explore
through direct interactions with the model hypothesis to fix it. We detail the
design of the tool, and case studies related to the exploit of the regression
to the mean bias and how it can be addressed, and how models are perturbed by
the vanish of landmarks such as bikes.
