  Many autonomous agents, such as intelligent vehicles, are inherently required
to interact with one another. Game theory provides a natural mathematical tool
for robot motion planning in such interactive settings. However, tractable
algorithms for such problems usually rely on a strong assumption, namely that
the objectives of all players in the scene are known. To make such tools
applicable for ego-centric planning with only local information, we propose an
adaptive model-predictive game solver, which jointly infers other players'
objectives online and computes a corresponding generalized Nash equilibrium
(GNE) strategy. The adaptivity of our approach is enabled by a differentiable
trajectory game solver whose gradient signal is used for maximum likelihood
estimation (MLE) of opponents' objectives. This differentiability of our
pipeline facilitates direct integration with other differentiable elements,
such as neural networks (NNs). Furthermore, in contrast to existing solvers for
cost inference in games, our method handles not only partial state observations
but also general inequality constraints. In two simulated traffic scenarios, we
find superior performance of our approach over both existing game-theoretic
methods and non-game-theoretic model-predictive control (MPC) approaches. We
also demonstrate our approach's real-time planning capabilities and robustness
in two hardware experiments.
