外观
论文arXiv 2610.0864022 天前
RIWANav：用于城市导航的递归世界-动作模型与自我改进
原文链接：RIWANav: Recursive World-Action Models with Self-Improvement for Urban Navigation（arXiv 2610.08640 · 发布日期：2026-10-06）
RIWANav: Recursive World-Action Models with Self-Improvement for Urban Navigation
针对长程城市导航误差累积，RIWANAV递归联合改进世界模型与策略，并在实验和真实世界试验中验证。
核心要点
- 长程城市导航依赖连续局部决策，单步错误会随时间累积。
- 框架交替更新世界模型与动作模型，用想象结果为GRPO提供比较反馈。
- 改进策略按行为新颖性和预测误差选择专家一致的动作-视频对，构建自课程。
- 实验显示其优于训练基线和既有方法，真实世界试验显示实用适用性。
验证范围与局限：验证包括实验和真实世界试验，摘要未说明具体平台、数量或任务规模。
英文摘要 (Abstract)
Long-horizon urban navigation requires sequential local decisions whose errors can compound over time. Imitation learning (IL) rarely learns from failures, while physical trial-and-error reinforcement learning (RL) is costly. Action-conditioned world models can provide imagined feedback by predicting visual consequences for candidate actions. However, a frozen world model may become less reliable as the policy evolves. In this paper, we introduce RIWANAV, a post-training framework that casts the coupled adaptation of a world model and an action model (policy) as task-specific recursive self-improvement (RSI). Each cycle alternates two updates. The world model evaluates policy actions through imagined outcomes, providing comparative feedback for group-relative policy optimization (GRPO). The improved policy then constructs a grounded self-curriculum, selecting expert-consistent action-video pairs by behavioral novelty and prediction error. The refined world model supplies feedback for the next policy update, closing the recursive self-improvement loop. Experiments show that RIWANAV outperforms training baselines and prior methods, validating the proposed recursive self-improvement loop between the policy and world model. Real-world trials further demonstrate its practical applicability.
作者：Jing Xie, Shouwei Ruan, Yubin Wang, Yuxiang Zhang, Haitao Yang, Songchang Jin, Dianxi Shi
来源
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