In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently reach known goals. This ability can be associated with spatial reasoning, where an agent is able to perceive spatial relationships and regularities, and discover object characteristics. In classical Reinforcement Learning (RL) setups, this capacity is learned from reward alone. We introduce supplementary supervision in the form of auxiliary tasks designed to favor the emergence of spatial perception capabilities in agents trained for a goal-reaching downstream objective. We show that learning to estimate metrics quantifying the spatial relationships between an agent at a given location and a goal to reach has a high positive impact in Multi-Object Navigation settings. Our method significantly improves the performance of different baseline agents, that either build an explicit or implicit representation of the environment, even matching the performance of incomparable oracle agents taking ground-truth maps as input.
翻译:在视觉导航方面,必须具备绘制新环境图的能力,才能使代理人在所考虑的地点利用其观测历史,并有效地达到已知目标。这种能力可以与空间推理相联系,因为代理人能够感知空间关系和规律,并发现物体特性。在典型的加强学习(RL)设置中,这种能力仅从奖励中学习。我们引入辅助性监督,其形式为辅助性任务,目的是在受过训练的代理人中培养空间感知能力,以达到目标性的下游目标。我们表明,在某一地点对代理人之间的空间关系进行量化和达到的目标进行估计,对多物体导航环境具有高度的积极影响。我们的方法极大地改进了不同基线代理人的性能,这些代理人可以建立对环境的直观或隐含的描述,甚至与以地面图作为输入的不相容或不相干的代理人的性能相匹配。