Meta Learning for Multi-View Visuomotor Systems

Benji Alwis 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. Introduction Inspired by how humans learn motor skills through trial and error, reinforcement learning is used in end-to-end visuomotor systems [1,2,3] to help robots master complex manipulation tasks based on raw sensory inputs, including visual observations. Online reinforcement learning is deemed impractical because robots need continuous interaction with the environment.