Do Robotic World Models Really Follow Actions? Diagnosing and Aligning Action-Conditioned Generation for Policy Learning

论文概要

研究领域: 机器人
作者: Sixiang Chen, Jiaming Liu, Jixian Wu, Yichen Guo, Tinghao Wang等
发布时间: 2026-08-25
arXiv: 2608.24885

中文摘要

基于动作条件的世界模型正越来越多地被用作策略评估和改进的学习模拟器，但其有效性建立在一个未经证实的假设之上：生成的未来能够忠实地反映任意有效动作。现有基准测试通常局限于专家演示，导致对非专家动作跟随能力的评估不足。为填补这一空白，我们提出了WorldEcho，它通过视觉完整性和SE(3)轨迹对齐，在更广泛的动作分布上探测动作跟随能力。我们的诊断显示，当前世界模型能够较好执行专家动作，但在处理多样化的非专家轨迹时表现不佳，要么忽略指令动作，要么产生视觉无效的推演。我们进一步提出了WorldSync，从三个互补维度强化动作跟随：分布覆盖、表征接地和干预效果对齐。它扩展了动作后果的训练分布，通过动作强制专家将中间视频表征接地于动作诱导的机器人动力学，并对动作干预下的预测变化与真实未来中的相应变化进行对齐。在RoboTwin基准和真实机器人任务上的实验表明，WorldSync改善了WorldEcho指标，并作为更可靠的模拟器用于迭代策略改进，使策略达到更高的成功率。

原文摘要

Action-conditioned world models are increasingly used as learned simulators for policy evaluation and improvement, yet their effectiveness rests on an unverified assumption: generated futures faithfully reflect arbitrary valid actions. Existing benchmarks are typically confined to expert demonstrations, leaving off-expert action following inadequately evaluated. To address this gap, we introduce WorldEcho, which probes action following over a broader action distribution using visual integrity and SE(3) trajectory alignment. Our diagnosis shows that current world models reasonably execute expert actions but struggle with diverse off-expert trajectories, either ignoring the commanded actions or producing visually invalid rollouts. We further propose WorldSync, which strengthens action follow...

自动采集于 2026-08-27

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