[论文] DreamerAD: Latent World Models with Shortcut Forcing for Efficient Aut...

论文概要

研究领域: Robotics 作者: Pengxuan Yang, Yupeng Zheng, Deheng Qian, Zebin Xing, Qichao Zhang... 发布时间: 2026-03-25 arXiv: 2603.24587

中文摘要

本文提出DreamerAD，首个潜在世界模型框架，通过将扩散采样从100步压缩到1步，实现80倍加速同时保持视觉可解释性，从而支持高效的自动驾驶强化学习。在真实驾驶数据上训练RL策略会产生高昂的成本和安全风险。虽然现有的像素级扩散世界模型支持安全的基于想象的训练，但它们遭受多步扩散推理延迟（2秒/帧）的困扰，阻碍了高频RL交互。

原文摘要

We introduce DreamerAD, the first latent world model framework that enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling from 100 steps to 1 - achieving 80x speedup while maintaining visual interpretability. Training RL policies on real-world driving data incurs prohibitive costs and safety risks. While existing pixel-level diffusion world models enable safe imagination-based training, they suffer from multi-step diffusion inference latency (2s/frame) that prevents high-frequency RL interaction.

--- *自动采集于 2026-03-27*

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