  The use of multi-camera views simultaneously has been shown to improve the
generalization capabilities and performance of visual policies. However, the
hardware cost and design constraints in real-world scenarios can potentially
make it challenging to use multiple cameras. In this study, we present a novel
approach to enhance the generalization performance of vision-based
Reinforcement Learning (RL) algorithms for robotic manipulation tasks. Our
proposed method involves utilizing a technique known as knowledge distillation,
in which a pre-trained ``teacher'' policy trained with multiple camera
viewpoints guides a ``student'' policy in learning from a single camera
viewpoint. To enhance the student policy's robustness against camera location
perturbations, it is trained using data augmentation and extreme viewpoint
changes. As a result, the student policy learns robust visual features that
allow it to locate the object of interest accurately and consistently,
regardless of the camera viewpoint. The efficacy and efficiency of the proposed
method were evaluated both in simulation and real-world environments. The
results demonstrate that the single-view visual student policy can successfully
learn to grasp and lift a challenging object, which was not possible with a
single-view policy alone. Furthermore, the student policy demonstrates
zero-shot transfer capability, where it can successfully grasp and lift objects
in real-world scenarios for unseen visual configurations.
