从多智能体博弈演示中学习约束，确保交互动作安全可靠。
Constraint Learning in Multi-Agent Dynamic Games from Demonstrations of Local Nash Interactions
- 用MILP建模博弈方的KKT条件，还原局部纳什均衡下的约束
- 恢复的约束可逼近真实安全与危险区域，理论保证可靠
- 适用于非凸、非线性系统的安全运动规划，适合机器人交互
我们提出一种基于逆动态博弈的算法，从多个智能体局部纳什均衡交互的数据集中学习参数化约束。具体而言，引入混合整数线性规划（MILP）来编码交互智能体的Karush-Kuhn-Tucker（KKT）条件，从而恢复与交互演示中局部纳什平稳性一致的约束。我们建立了理论保证，证明该方法能学习到真实安全集和危险集的内逼近。此外，利用本方法恢复的交互约束，设计了鲁棒满足底层约束的运动规划。在仿真和硬件实验中，我们的方法准确推断出多种类型约束（包括凸与非凸），并为具有非线性动力学的智能体设计了安全的交互运动规划。
原文摘要 · Abstract (English)
We present an inverse dynamic game-based algorithm to learn parametric constraints from a given dataset of local Nash equilibrium interactions between multiple agents. Specifically, we introduce mixed-integer linear programs (MILP) encoding the Karush-Kuhn-Tucker (KKT) conditions of the interacting agents, which recover constraints consistent with the local Nash stationarity of the interaction demonstrations. We establish theoretical guarantees that our method learns inner approximations of the true safe and unsafe sets. We also use the interaction constraints recovered by our method to design motion plans that robustly satisfy the underlying constraints. Across simulations and hardware experiments, our methods accurately inferred constraints and designed safe interactive motion plans for various classes of constraints, both convex and non-convex, from interaction demonstrations of agents with nonlinear dynamics.
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