World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning
Overview
This research introduces Adversarial World Modeling (AWM), a theoretically grounded framework that casts adversarially robust planner learning as a constrained min-max game. By decoupling the solver, AWM converts a planner's predictive world model into a role-conditioned adversary for inner minimization and optimizes a regret-aware robust best response for outer maximization. Experiments on nuPlan and InterPlan benchmarks demonstrate that AWM generates transferable adversarial interactions and achieves competitive closed-loop performance in both nominal and highly interactive long-tail scenarios.
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- arXiv Robotics — research abstractsSignalWorld Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning
This research introduces Adversarial World Modeling (AWM), a theoretically grounded framework that casts adversarially robust planner learning as a constrained min-max game. By decoupling the solver, AWM converts a planner's predictive world model into a role-conditioned adversary for inner minimization and optimizes a regret-aware robust best response for outer maximization. Experiments on nuPlan and InterPlan benchmarks demonstrate that AWM generates transferable adversarial interactions and achieves competitive closed-loop performance in both nominal and highly interactive long-tail scenarios.
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