Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work, we take a game-theoretic perspective on contingency planning which is tailored to multi-agent scenarios in which a robot's actions impact the decisions of other agents and vice versa. The resulting contingency game allows the robot to efficiently coordinate with other agents by generating strategic motion plans conditioned on multiple possible intents for other actors in the scene. Contingency games are parameterized via a scalar variable which represents a future time at which intent uncertainty will be resolved. Varying this parameter enables a designer to easily adjust how conservatively the robot behaves in the game. Interestingly, we also find that existing variants of game-theoretic planning under uncertainty are readily obtained as special cases of contingency games. Lastly, we offer an efficient method for solving N-player contingency games with nonlinear dynamics and non-convex costs and constraints. Through a series of simulated autonomous driving scenarios, we demonstrate that plans generated via contingency games provide quantitative performance gains over game-theoretic motion plans that do not account for future uncertainty reduction.

翻译：应急规划（contingency planning）是指智能体根据不确定事件的结果生成一组可能计划的策略，这已成为机器人在不确定环境下行动日益流行的方式。本研究从博弈论视角审视应急规划，特别适用于机器人行为影响其他智能体决策且反之亦然的多人场景。由此产生的应急博弈使机器人能够通过生成基于场景中多个可能意图的策略性运动规划来高效协调其他智能体。应急博弈通过标量变量进行参数化，该变量代表意图不确定性将得到解决的未来时间点。调整该参数使设计者能够轻松调节机器人在博弈中的保守程度。有趣的是，我们还发现现有不确定环境下的博弈论规划变体均可作为应急博弈的特例轻松获得。最后，我们提出一种高效方法，用于求解具有非线性动力学、非凸成本与约束条件的N人应急博弈。通过一系列模拟自动驾驶场景，我们证明了基于应急博弈生成的规划相比未考虑未来不确定性降低的博弈论运动规划能带来量化性能提升。