This work studies object goal navigation task, which involves navigating to the closest object related to the given semantic category in unseen environments. Recent works have shown significant achievements both in the end-to-end Reinforcement Learning approach and modular systems, but need a big step forward to be robust and optimal. We propose a hierarchical method that incorporates standard task formulation and additional area knowledge as landmarks, with a way to extract these landmarks. In a hierarchy, a low level consists of separately trained algorithms to the most intuitive skills, and a high level decides which skill is needed at this moment. With all proposed solutions, we achieve a 0.75 success rate in a realistic Habitat simulator. After a small stage of additional model training in a reconstructed virtual area at a simulator, we successfully confirmed our results in a real-world case.
翻译:这份工作研究的目标导航任务涉及在无形环境中探索到与特定语义类别有关的最接近的对象。最近的工作显示,在端到端强化学习方法和模块化系统中都取得了显著成就,但需要向前迈出一大步才能做到稳健和最佳。我们提出了一个等级方法,将标准任务拟订和更多地区知识作为里程碑,以提取这些里程碑。在一个等级中,一个低层次由分别培训的最直观技能的算法组成,以及一个高层次决定目前需要哪些技能。我们利用所有拟议的解决方案,在现实的人居模拟器中取得了0.75的成功率。在模拟器中,在经过一个经过一个小阶段在重建后的虚拟区域进行额外示范培训的模拟器之后,我们在现实世界案例中成功地确认了我们的成果。