  Modern robots require accurate forecasts to make optimal decisions in the
real world. For example, self-driving cars need an accurate forecast of other
agents' future actions to plan safe trajectories. Current methods rely heavily
on historical time series to accurately predict the future. However, relying
entirely on the observed history is problematic since it could be corrupted by
noise, have outliers, or not completely represent all possible outcomes. To
solve this problem, we propose a novel framework for generating robust
forecasts for robotic control. In order to model real-world factors affecting
future forecasts, we introduce the notion of an adversary, which perturbs
observed historical time series to increase a robot's ultimate control cost.
Specifically, we model this interaction as a zero-sum two-player game between a
robot's forecaster and this hypothetical adversary. We show that our proposed
game may be solved to a local Nash equilibrium using gradient-based
optimization techniques. Furthermore, we show that a forecaster trained with
our method performs 30.14% better on out-of-distribution real-world lane change
data than baselines.
