Meta Learning for Multi-View Visuomotor Systems

Abstract

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.

Change of authors since further experiments based on second and third authors comments will be added to a future version of this paper