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Zhejiang University's GuardPIBT enhances PIBT with residual reranking, completes conflict-free 3D planning for 1 million agents in simulation

A Zhejiang University team proposed GuardPIBT, which, while retaining the PIBT execution kernel, uses a neural network to make residual reranking proposals over PIBT's native candidate actions; conflict checking, priority inheritance, and backtracking are still handled by the PIBT kernel, and counterfactual grouped gating is introduced to filter harmful interventions.

Part of the event: Zhejiang University proposes GuardPIBT, enhancing PIBT planning with residual re-ranking

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