Visual Reinforcement Learning (RL) agents trained on limited views face
significant challenges in generalizing their learned abilities to unseen views.
This inherent difficulty is known as the problem of $\textit{view
generalization}$. In this work, we systematically categorize this fundamental
problem into four distinct and highly challenging scenarios that closely
resemble real-world situations. Subsequently, we propose a straightforward yet
effective approach to enable successful adaptation of visual
$\textbf{Mo}$del-based policies for $\textbf{Vie}$w generalization
($\textbf{MoVie}$) during test time, without any need for explicit reward
signals and any modification during training time. Our method demonstrates
substantial advancements across all four scenarios encompassing a total of
$\textbf{18}$ tasks sourced from DMControl, xArm, and Adroit, with a relative
improvement of $\mathbf{33}$%, $\mathbf{86}$%, and $\mathbf{152}$%
respectively. The superior results highlight the immense potential of our
approach for real-world robotics applications. Videos are available at
this https URL .

本文提出了一种名为 MoVie 的视觉模型策略适应方法，通过在测试期间实现视图泛化，无需任何明确的奖励信号和任何训练期间的修改，可显著提高目标任务的性能表现，这表明该方法在实际中应用于机器人技术具有巨大的潜力。