  Anchor-bolt insertion is a peg-in-hole task performed in the construction
field for holes in concrete. Efforts have been made to automate this task, but
the variable lighting and hole surface conditions, as well as the requirements
for short setup and task execution time make the automation challenging. In
this study, we introduce a vision and proprioceptive data-driven robot control
model for this task that is robust to challenging lighting and hole surface
conditions. This model consists of a spatial attention point network (SAP) and
a deep reinforcement learning (DRL) policy that are trained jointly end-to-end
to control the robot. The model is trained in an offline manner, with a
sample-efficient framework designed to reduce training time and minimize the
reality gap when transferring the model to the physical world. Through
evaluations with an industrial robot performing the task in 12 unknown holes,
starting from 16 different initial positions, and under three different
lighting conditions (two with misleading shadows), we demonstrate that SAP can
generate relevant attention points of the image even in challenging lighting
conditions. We also show that the proposed model enables task execution with
higher success rate and shorter task completion time than various baselines.
Due to the proposed model's high effectiveness even in severe lighting, initial
positions, and hole conditions, and the offline training framework's high
sample-efficiency and short training time, this approach can be easily applied
to construction.
