A Data-Driven Large-Scale Optimization Approach for Task-Specific Physics Realism in Real-Time Robotics Simulation

Physics-based simulation of robots requires mod-
els of the simulated robots and their environment. For a realistic
simulation behavior, these models must be accurate. Their
physical properties such as geometric and kinematic values,
as well as dynamic parameters such as mass, inertia matrix
and friction, must be modelled. Unfortunately, this problem is
hard for at least two reasons. First, physics engines designed
for simulation of rigid bodies in real-time cannot accurately
describe many common real world phenomena, e.g. (drive)
friction and grasping. Second, classical parameter identification
algorithms are well-studied and efficient, but often necessitate
significant manual engineering effort and may not be applicable
due to application constraints. Thus, we present a data-
driven general purpose tool, which allows to optimize model
parameters for (task-specific) realistic simulation behavior. Our
approach directly uses the simulator and the model under
optimization to improve model parameters. The optimization
process is highly distributed and uses a hybrid optimization
approach based on metaheuristics and the Ceres non-linear
least squares solver. The user only has to provide a configuration
file that specifies which model parameter to optimize together
with realism criteri

• Robotics