视频规划中在线更新模型，失败后自动重规划。
Implicit State Estimation via Video Replanning
- 交互时用新数据在线更新模型，动态调整计划。
- 在新模拟操作基准上显著提升重规划性能。
- 适合需要自适应决策的复杂交互任务研究者。
基于视频的表征在规划与决策中日益重要，因其能编码丰富的时空动态和几何关系，适用于物体操作与导航等复杂任务。然而，现有视频规划框架在交互时因无法推理部分观测环境中的不确定性，难以应对失败。为此，我们提出一种新框架，将交互时的数据融入规划过程，在线更新模型参数并过滤先前失败的计划。该方法实现隐式状态估计，使系统无需显式建模未知状态变量即可动态适应。我们在一个全新的模拟操作基准上进行了广泛实验，验证了该框架在提升重规划性能方面的有效性，推动了基于视频决策的发展。
原文摘要 · Abstract (English)
Video-based representations have gained prominence in planning and decision-making due to their ability to encode rich spatiotemporal dynamics and geometric relationships. These representations enable flexible and generalizable solutions for complex tasks such as object manipulation and navigation. However, existing video planning frameworks often struggle to adapt to failures at interaction time due to their inability to reason about uncertainties in partially observed environments. To overcome these limitations, we introduce a novel framework that integrates interaction-time data into the planning process. Our approach updates model parameters online and filters out previously failed plans during generation. This enables implicit state estimation, allowing the system to adapt dynamically without explicitly modeling unknown state variables. We evaluate our framework through extensive experiments on a new simulated manipulation benchmark, demonstrating its ability to improve replanning performance and advance the field of video-based decision-making.
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