  In recent years, computer vision tasks like target tracking and human pose
estimation have immensely benefited from synthetic data generation and novel
rendering techniques. On the other hand, methods in robotics, especially for
robot perception, have been slow to leverage these techniques. This is because
state-of-the-art simulation frameworks for robotics lack either complete
control, integration with the Robot Operating System (ROS), realistic physics
or photorealism. To solve this, we present a fully customizable framework for
generating realistic animated dynamic environments (GRADE) for robotics
research, focused primarily at robot perception. The framework can be used
either to generate ground truth data for robotic vision-related tasks and
offline processing, or to experiment with robots online in dynamic
environments. We build upon the Nvidia Isaac Sim to allow control of custom
robots. We provide methods to include assets, populate and control the
simulation, and process the data. Using autonomous robots in GRADE, we generate
video datasets of an indoor dynamic environment. First, we use it to
demonstrate the framework's visual realism by evaluating the sim-to-real gap
through experiments with YOLO and Mask R-CNN. Second, we benchmark dynamic SLAM
algorithms with this dataset. This not only shows that GRADE can significantly
improve training performance and generalization to real sequences, but also
highlights how current dynamic SLAM methods over-rely on known benchmarks,
failing to generalize. We also introduce a method to precisely repeat a
previously recorded experiment, while allowing changes in the surroundings of
the robot. Code and data are provided as open-source at
https://grade.is.tue.mpg.de.
