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arXiv AI By Seong-Eun Hong, JuYeong Hwang, Jinhyun Kim, Hanyoung Jang, HyeongYeop Kang

Seeing Beyond the Next Step: World-Model-Guided Human-Like Navigation in Multi-Agent Scenes

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The paper proposes using world models only during training to distill foresight into a fast navigation policy, rather than deploying them online. By combining a visual action prior with two task‑aligned world models—one for social dynamics and one for scene semantics—the authors score candidate actions based on predicted consequences and refine the policy through group‑relative preference distillation. The resulting policy, which runs in a single forward pass at deployment, outperforms reactive and learned baselines in safety, social coherence, and goal‑directedness across five held‑out scenarios, and observers rate its motion as more human‑like.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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