In many real-world visual Imitation Learning (IL) scenarios, there is a misalignment between the agent's and the expert's perspectives, which might lead to the failure of imitation. Previous methods have generally solved this problem by domain alignment, which incurs extra computation and storage costs, and these methods fail to handle the \textit{hard cases} where the viewpoint gap is too large. To alleviate the above problems, we introduce active sensoring in the visual IL setting and propose a model-based SENSory imitatOR (SENSOR) to automatically change the agent's perspective to match the expert's. SENSOR jointly learns a world model to capture the dynamics of latent states, a sensor policy to control the camera, and a motor policy to control the agent. Experiments on visual locomotion tasks show that SENSOR can efficiently simulate the expert's perspective and strategy, and outperforms most baseline methods.

通过引入主动传感器技术，本论文在视觉模仿学习 (IL) 的场景中解决了代理人与专家视角不对齐的问题，提出了基于模型的传感器模仿器 (SENSOR) 来自动调整代理人的视角以匹配专家，实验证明 SENSOR 可以有效地模拟专家的视角和策略，并超过大多数基准方法。

传感器：通过主动感知模仿第三人称专家的行为