  Robot control for tactile feedback-based manipulation can be difficult due to
the modeling of physical contacts, partial observability of the environment,
and noise in perception and control. This work focuses on solving partial
observability of contact-rich manipulation tasks as a Sequence-to-Sequence
(Seq2Seq)} Imitation Learning (IL) problem. The proposed Seq2Seq model produces
a robot-environment interaction sequence to estimate the partially observable
environment state variables. Then, the observed interaction sequence is
transformed to a control sequence for the task itself. The proposed Seq2Seq IL
for tactile feedback-based manipulation is experimentally validated on a
door-open task in a simulated environment and a snap-on insertion task with a
real robot. The model is able to learn both tasks from only 50 expert
demonstrations, while state-of-the-art reinforcement learning and imitation
learning methods fail.
