  Planning safe robot motions in the presence of humans requires reliable
forecasts of future human motion. However, simply predicting the most likely
motion from prior interactions does not guarantee safety. Such forecasts fail
to model the long tail of possible events, which are rarely observed in limited
datasets. On the other hand, planning for worst-case motions leads to overtly
conservative behavior and a "frozen robot". Instead, we aim to learn forecasts
that predict counterfactuals that humans guard against. We propose a novel
game-theoretic framework for joint planning and forecasting with the payoff
being the performance of the planner against the demonstrator, and present
practical algorithms to train models in an end-to-end fashion. We demonstrate
that our proposed algorithm results in safer plans in a crowd navigation
simulator and real-world datasets of pedestrian motion. We release our code at
https://github.com/portal-cornell/Game-Theoretic-Forecasting-Planning.
