SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring
Kaichen Huang Minghao Shao Shenghua Wan Hai-Hang Sun Shuai Feng Le Gan De-Chuan Zhan
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.