AI RESEARCH
MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving
arXiv CS.CV
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ArXi:2605.14201v1 Announce Type: cross Vision-language-action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings due to being trained under traditional imitation learning framework. Existing closed-loop supervision approaches lack scalability and fail to completely model a reactive environment. We propose MAPLE, a novel framework for reactive, multi-agent rollout of a dynamic driving scenario in the latent space of the VLA model.