Learning to Play Trajectory Games Against Opponents with Unknown Objectives

Learning to Play Trajectory Games Against Opponents with Unknown Objectives

Liu, Xinjie (Student TU Delft)
Peters, L. (TU Delft Learning & Autonomous Control)
Alonso-Mora, J. (TU Delft Learning & Autonomous Control)

2023

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-player hardware experiments.

Collision avoidance
Games
human-aware motion planning
integrated planning and learning
Maximum likelihood estimation
multi-robot systems
Optimization
Planning
Robots
Trajectory
Trajectory games

http://resolver.tudelft.nl/uuid:fafde1ec-a419-4ad7-817e-d8a4e384ca6c

https://doi.org/10.1109/LRA.2023.3280809

2023-11-29

2377-3766

IEEE Robotics and Automation Letters, 8 (7), 4139-4146

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Xinjie Liu, L. Peters, J. Alonso-Mora