This work presents an embodied agent that can adapt its semantic segmentation network to new indoor environments in a fully autonomous way. Because semantic segmentation networks fail to generalize well to unseen environments, the agent collects images of the new environment which are then used for self-supervised domain adaptation. We formulate this as an informative path planning problem, and present a novel information gain that leverages uncertainty extracted from the semantic model to safely collect relevant data. As domain adaptation progresses, these uncertainties change over time and the rapid learning feedback of our system drives the agent to collect different data. Experiments show that our method adapts to new environments faster and with higher final performance compared to an exploration objective, and can successfully be deployed to real-world environments on physical robots.

翻译：这项工作展示了能够以完全自主的方式将其语义分割网改造为新的室内环境的体现剂。 由于语义分割网无法很好地向看不见的环境推广, 语义分割网收集了新环境的图像, 然后用于自我监督的域适应。 我们将此发展成信息化路径规划问题, 并展示了一种新的信息收益, 利用语义模型的不确定性安全收集相关数据。 随着域适应的进展, 这些不确定性随时间而变化, 以及我们系统的快速学习反馈, 促使该代理收集了不同数据。 实验显示, 我们的方法适应新环境的速度更快, 最终性能比探索目标要高, 并且能够成功地在物理机器人上被应用到现实世界环境中。