We propose a learning-based navigation system for reaching visually indicated
goals and demonstrate this system on a real mobile robot platform. Learning
provides an appealing alternative to conventional methods for robotic
navigation: instead of reasoning about environments in terms of geometry and
maps, learning can enable a robot to learn about navigational affordances,
understand what types of obstacles are traversable (e.g., tall grass) or not
(e.g., walls), and generalize over patterns in the environment. However, unlike
conventional planning algorithms, it is harder to change the goal for a learned
policy during deployment. We propose a method for learning to navigate towards
a goal image of the desired destination. By combining a learned policy with a
topological graph constructed out of previously observed data, our system can
determine how to reach this visually indicated goal even in the presence of
variable appearance and lighting. Three key insights, waypoint proposal, graph
pruning and negative mining, enable our method to learn to navigate in
real-world environments using only offline data, a setting where prior methods
struggle. We instantiate our method on a real outdoor ground robot and show
that our system, which we call ViNG, outperforms previously-proposed methods
for goal-conditioned reinforcement learning, including other methods that
incorporate reinforcement learning and search. We also study how \sysName
generalizes to unseen environments and evaluate its ability to adapt to such an
environment with growing experience. Finally, we demonstrate ViNG on a number
of real-world applications, such as last-mile delivery and warehouse
inspection. We encourage the reader to visit the project website for videos of
our experiments and demonstrations sites.google.com/view/ving-robot.

提出了一个基于学习的导航系统，该系统应用于实际移动机器人平台，旨在使机器人通过学习来了解环境和导航便利性，以便在视觉引导下实现目标点的导航，经过实地测试和应用，该系统的表现优于其他以强化学习和搜索为基础的方法。

ViNG: 使用视觉目标学习开放世界导航

ViNG: Learning Open-World Navigation with Visual Goals

This work studies the problem of object goal navigation which involves
navigating to an instance of the given object category in unseen environments.
End-to-end learning-based navigation methods struggle at this task as they are
ineffective at exploration and long-term planning. We propose a modular system
called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic
map and uses it to explore the environment efficiently based on the goal object
category. Empirical results in visually realistic simulation environments show
that the proposed model outperforms a wide range of baselines including
end-to-end learning-based methods as well as modular map-based methods and led
to the winning entry of the CVPR-2020 Habitat ObjectNav Challenge. Ablation
analysis indicates that the proposed model learns semantic priors of the
relative arrangement of objects in a scene, and uses them to explore
efficiently. Domain-agnostic module design allow us to transfer our model to a
mobile robot platform and achieve similar performance for object goal
navigation in the real-world.

该研究提出了 Goal-Oriented Semantic Exploration 系统，通过构建基于目标对象类别的情节语义地图来有效探索环境，该模型在模拟环境和移动机器人平台中实现了物体目标导航的优异表现。