  Traditional visual servoing methods suffer from serving between scenes from
multiple perspectives, which humans can complete with visual signals alone. In
this paper, we investigated how multi-perspective visual servoing could be
solved under robot-specific constraints, including self-collision, singularity
problems. We presented a novel learning-based multi-perspective visual servoing
framework, which iteratively estimates robot actions from latent space
representations of visual states using reinforcement learning. Furthermore, our
approaches were trained and validated in a Gazebo simulation environment with
connection to OpenAI/Gym. Through simulation experiments, we showed that our
method can successfully learn an optimal control policy given initial images
from different perspectives, and it outperformed the Direct Visual Servoing
algorithm with mean success rate of 97.0%.
