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.

翻译：本文提出了一种新方法，使机器人多视角视动系统能够快速适应与基准设置不同的摄像机配置。该方法利用元学习对感知网络进行微调，同时保持策略网络固定。实验结果表明，达到基准性能所需的新训练回合数显著减少。