  We need intelligent robots for mobile construction, the process of navigating
in an environment and modifying its structure according to a geometric design.
In this task, a major robot vision and learning challenge is how to exactly
achieve the design without GPS, due to the difficulty caused by the
bi-directional coupling of accurate robot localization and navigation together
with strategic environment manipulation. However, many existing robot vision
and learning tasks such as visual navigation and robot manipulation address
only one of these two coupled aspects. To stimulate the pursuit of a generic
and adaptive solution, we reasonably simplify mobile construction as a
partially observable Markov decision process (POMDP) in 1/2/3D grid worlds and
benchmark the performance of a handcrafted policy with basic localization and
planning, and state-of-the-art deep reinforcement learning (RL) methods. Our
extensive experiments show that the coupling makes this problem very
challenging for those methods, and emphasize the need for novel task-specific
solutions.
