Learning Conditional Error Model for Simulated Time-Series Data

Applications such as autonomous navigation [1], human-robot interaction [2], game-playing robots [8], etc., use simulation to minimize the cost of testing in real world. Furthermore, some machine learning algorithms, like reinforcement learning, use simulation for training a model. To test reliably in simulation or deploy a model in the real world that is trained with simulated data, the simulator should be representative of the real environment. Usually, the simulator is based on manually designed rules and ignores the stochastic behavior of measurements. In particular, we would like to learn a model that captures uncertainties of the sensing algorithms (e.g. neural networks used to detect objects) in real world and add them in simulation. We model the distribution of residuals between the ground truth states of the objects and their perceived states by the sensing algorithm. This error distribution depends both on the current state of the object (e.g. distance from the sensor) and its past residuals. We assume the error distribution is conditionally Gaussian, and we use a deep neural neural network (DNN) to map the object states and past residuals to the distribution parameters (mean and variance). Our conditional model perturbs the dynamic objects’ states (position, velocities, orientations, and shape) and produces smoother trajectories which look similar to the real data.