标题： 利用测试时观测干预的重新想象：视觉模型预测控制的干扰鲁棒世界模型预测 显示英文标题

标题： Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control

摘要： 世界模型使机器人能够根据当前观测和计划的动作“想象”未来的观测，这些模型已被越来越多地用作广义的动力学模型，以促进机器人学习。 尽管它们前景广阔，但当遇到新颖的视觉干扰因素（如训练期间很少看到的对象和背景元素）时，这些模型仍然很脆弱。 具体来说，新颖的干扰因素会破坏动作结果预测，导致机器人依赖世界模型想象进行规划或动作验证时出现下游失败。 在这项工作中，我们提出了重想象与观察干预（ReOI），这是一种简单而有效的测试时间策略，使世界模型能够在开放世界场景中预测更可靠的动作结果，在这种场景中不可避免地存在新颖且未预料到的视觉干扰因素。 给定当前机器人的观测，ReOI首先通过识别场景中的哪些元素在世界模型预测过程中以物理上不可能的方式退化来检测视觉干扰因素。 然后，它修改当前观测以去除这些干扰因素，并使观测更接近训练分布。 最后，ReOI使用修改后的观测重新想象未来的结果，并在事后重新引入干扰因素，以保持下游规划和验证的视觉一致性。 我们在一组机器人操作任务中验证了我们的方法，这些任务涉及动作验证，其中验证器需要根据世界模型的预测选择期望的动作计划。 我们的结果显示，ReOI对分布内和分布外的视觉干扰因素都具有鲁棒性。 值得注意的是，在存在新颖干扰因素的情况下，任务成功率提高了多达3倍，显著优于依赖世界模型预测而不进行想象干预的动作验证。 显示英文摘要

摘要： World models enable robots to "imagine" future observations given current observations and planned actions, and have been increasingly adopted as generalized dynamics models to facilitate robot learning. Despite their promise, these models remain brittle when encountering novel visual distractors such as objects and background elements rarely seen during training. Specifically, novel distractors can corrupt action outcome predictions, causing downstream failures when robots rely on the world model imaginations for planning or action verification. In this work, we propose Reimagination with Observation Intervention (ReOI), a simple yet effective test-time strategy that enables world models to predict more reliable action outcomes in open-world scenarios where novel and unanticipated visual distractors are inevitable. Given the current robot observation, ReOI first detects visual distractors by identifying which elements of the scene degrade in physically implausible ways during world model prediction. Then, it modifies the current observation to remove these distractors and bring the observation closer to the training distribution. Finally, ReOI "reimagines" future outcomes with the modified observation and reintroduces the distractors post-hoc to preserve visual consistency for downstream planning and verification. We validate our approach on a suite of robotic manipulation tasks in the context of action verification, where the verifier needs to select desired action plans based on predictions from a world model. Our results show that ReOI is robust to both in-distribution and out-of-distribution visual distractors. Notably, it improves task success rates by up to 3x in the presence of novel distractors, significantly outperforming action verification that relies on world model predictions without imagination interventions.