In this work, we develop a scalable, local trajectory optimization algorithm that enables robots to interact with other robots. It has been shown that agents' interactions can be successfully captured in game-theoretic formulations, where the interaction outcome can be best modeled via the equilibria of the underlying dynamic game. However, it is typically challenging to compute equilibria of dynamic games as it involves simultaneously solving a set of coupled optimal control problems. Existing solvers operate in a centralized fashion and do not scale up tractably to multiple interacting agents. We enable scalable distributed game-theoretic planning by leveraging the structure inherent in multi-agent interactions, namely, interactions belonging to the class of dynamic potential games. Since equilibria of dynamic potential games can be found by minimizing a single potential function, we can apply distributed and decentralized control techniques to seek equilibria of multi-agent interactions in a scalable and distributed manner. We compare the performance of our algorithm with a centralized interactive planner in a number of simulation studies and demonstrate that our algorithm results in better efficiency and scalability. We further evaluate our method in hardware experiments involving multiple quadcopters.

翻译：本文提出了一种可扩展的局部轨迹优化算法，使机器人能够与其他机器人进行交互。已有研究表明，智能体间的交互可通过博弈论建模得到有效表征，其交互结果可通过底层动态博弈的均衡解最佳刻画。然而，动态博弈的均衡求解通常极具挑战性，因其需要同时求解一组耦合的最优控制问题。现有求解器采用集中式框架运作，难以在可扩展性条件下处理多智能体交互场景。我们通过利用多智能体交互中固有的结构特性——即属于动态势能博弈类别的交互——实现了可扩展的分布式博弈规划。由于动态势能博弈的均衡解可通过最小化单势能函数获得，我们能够采用分布式与去中心化控制技术，以可扩展且分布式的方式求解多智能体交互的均衡解。通过一系列仿真研究，我们将所提算法与集中式交互规划器进行性能对比，结果表明该算法具有更优的效率和可扩展性。我们还在涉及多架四旋翼飞行器的硬件实验中进一步验证了该方法的有效性。