  This paper introduces a new approach for quickly adapting a multi-view
visuomotor system for robots to varying camera configurations from the baseline
setup. It utilises meta-learning to fine-tune the perceptual network while
keeping the policy network fixed. Experimental results demonstrate a
significant reduction in the number of new training episodes needed to attain
baseline performance.
