未知の目的を持つ相手との軌道ゲームを学習する適応型モデル予測ゲームソルバー
Learning to Play Trajectory Games Against Opponents with Unknown Objectives
相手の目的が未知の状況で、オンラインで目的を推定しながらゲーム理論に基づく軌道計画を行う適応型モデル予測ゲームソルバーを提案し、交通シナリオと実機実験で有効性を示した。
著者: Xinjie Liu, Lasse Peters, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in the scene are known. To make such tools applicable for ego-centric planning with only local information, we propose an adaptive model-predictive game solver, which jointly infers other players' objectives online and computes a corresponding generalized Nash equilibrium (GNE) strategy. The adaptivity of our approach is enabled by a differentiable trajectory game solver whose gradient signal is used for maximum likelihood estimation (MLE) of opponents' objectives. This differentiability of our pipeline facilitates direct integration with other differentiable elements, such as neural networks (NNs). Furthermore, in contrast to existing solvers for cost inference in games, our method handles not only partial state observations but also general inequality constraints. In two simulated traffic scenarios, we find superior performance of our approach over both existing game-theoretic methods and non-game-theoretic model-predictive control (MPC) approaches. We also demonstrate our approach's real-time planning capabilities and robustness in two hardware experiments.
