The budgeted information gathering problem - where a robot with a fixed fuel
budget is required to maximize the amount of information gathered from the
world - appears in practice across a wide range of applications in autonomous
exploration and inspection with mobile robots. Although there is an extensive
amount of prior work investigating effective approximations of the problem,
these methods do not address the fact that their performance is heavily
dependent on distribution of objects in the world. In this paper, we attempt to
address this issue by proposing a novel data-driven imitation learning
framework.
We present an efficient algorithm, EXPLORE, that trains a policy on the
target distribution to imitate a clairvoyant oracle - an oracle that has full
information about the world and computes non-myopic solutions to maximize
information gathered. We validate the approach on a spectrum of results on a
number of 2D and 3D exploration problems that demonstrates the ability of
EXPLORE to adapt to different object distributions. Additionally, our analysis
provides theoretical insight into the behavior of EXPLORE. Our approach paves
the way forward for efficiently applying data-driven methods to the domain of
information gathering.

本篇论文研究的是一个应用于移动机器人自主探索和检查的问题，即在燃料预算有限的情况下最大化从世界中获取的信息量的问题。作者提出了一种新颖的数据驱动的模仿学习框架，并通过 EXPLORE 算法来训练一个策略以模仿一个有完整信息并计算非简约解来最大化获取信息的先知，验证了这一方法在不同对象分布上的适应能力。