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：应急规划是一种让智能体根据不确定事件的结果生成一组可能计划的策略，日益成为机器人在不确定性下行动的主流方法。本文从博弈论视角审视应急规划，特别针对机器人行为影响其他智能体决策、同时受其他智能体决策影响的多人场景。由此产生的应急博弈使机器人能够通过生成依赖于场景中其他参与者多种可能意图的策略性运动计划，高效地与其他智能体协调。应急博弈通过一个标量变量参数化，该变量代表意图不确定性将在未来某个时刻得以解决。改变这一参数使设计者能够轻松调整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性下博弈论规划的各种变体可自然作为应急博弈的特例得到。最后，我们提出了一种高效方法，用于求解具有非线性动力学、非凸成本及约束的N人应急博弈。通过一系列模拟自动驾驶场景，我们证明应急博弈生成的计划相比未考虑未来不确定性降低的博弈论运动计划，在量化性能上具有显著优势。