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, 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 interact 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 when intent uncertainty will be resolved. By estimating this parameter online, we construct a game-theoretic motion planner that adapts to changing beliefs while anticipating future certainty. We show that existing variants of game-theoretic planning under uncertainty are readily obtained as special cases of contingency games, and 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），即智能体依据不确定事件的结果生成一组备选计划，是机器人在不确定性下行动的日益流行的方法。本文从博弈论视角出发，针对多主体场景（其中机器人的行动影响其他主体的决策，反之亦然）对应急规划进行专门研究。由此产生的应急博弈允许机器人通过生成基于场景中其他参与者多种可能意图的策略性运动计划，高效地与其他主体进行交互。应急博弈通过一个标量变量进行参数化，该变量表示意图不确定性将被解决的未来时间点。通过在线估计此参数，我们构建了一个能够适应信念变化并预测未来确定性的博弈论运动规划器。我们证明了现有不确定性下博弈论规划方法的变体可视为应急博弈的特例，并提出了一种求解含非线性动力学、非凸代价和约束的N主体应急博弈的高效方法。通过一系列模拟自动驾驶场景，我们证明基于应急博弈生成的计划在量化性能上优于未考虑未来不确定性减少的博弈论运动规划。