Meta Learning for Multi-View Visuomotor Systems
This addresses the challenge of quickly adapting robotic systems to varying camera setups, though it appears incremental as it builds on existing meta-learning and visuomotor methods.
The paper tackles the problem of adapting multi-view visuomotor systems for robots to different camera configurations by using meta-learning to fine-tune the perceptual network, resulting in a significant reduction in the number of new training episodes required to achieve baseline performance.
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.