In reality, it is often more efficient to ask for help than to search the
entire space to find an object with an unknown location. We present a learning
framework that enables an agent to actively ask for help in such embodied
visual navigation tasks, where the feedback informs the agent of where the goal
is in its view. To emulate the real-world scenario that a teacher may not
always be present, we propose a training curriculum where feedback is not
always available. We formulate an uncertainty measure of where the goal is and
use empirical results to show that through this approach, the agent learns to
ask for help effectively while remaining robust when feedback is not available.

提出一种学习框架，使代理程序在具有未知位置的目标的肢体视觉导航任务中积极寻求帮助并通过不断的实践训练，在不总能得到反馈的情况下学会了有效地寻求帮助和提供足够的鲁棒性

一个在具体可视化导航中寻求帮助学习框架的研究

Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigation

"Embodied visual navigation" problem requires an agent to navigate in a 3D
environment mainly rely on its first-person observation. This problem has
attracted rising attention in recent years due to its wide application in
autonomous driving, vacuum cleaner, and rescue robot. A navigation agent is
supposed to have various intelligent skills, such as visual perceiving,
mapping, planning, exploring and reasoning, etc. Building such an agent that
observes, thinks, and acts is a key to real intelligence. The remarkable
learning ability of deep learning methods empowered the agents to accomplish
embodied visual navigation tasks. Despite this, embodied visual navigation is
still in its infancy since a lot of advanced skills are required, including
perceiving partially observed visual input, exploring unseen areas, memorizing
and modeling seen scenarios, understanding cross-modal instructions, and
adapting to a new environment, etc. Recently, embodied visual navigation has
attracted rising attention of the community, and numerous works has been
proposed to learn these skills. This paper attempts to establish an outline of
the current works in the field of embodied visual navigation by providing a
comprehensive literature survey. We summarize the benchmarks and metrics,
review different methods, analysis the challenges, and highlight the
state-of-the-art methods. Finally, we discuss unresolved challenges in the
field of embodied visual navigation and give promising directions in pursuing
future research.

综述了目前关于具有各种智能技能的智能机器人在 3D 环境中进行导航的研究领域，着重介绍了在这一领域内的各种高级技能，例如：感知局部观察视觉输入，理解跨模态的指令等等，并探讨了未来的研究方向及挑战。