  Learning object manipulation is a critical skill for robots to interact with
their environment. Even though there has been significant progress in robotic
manipulation of rigid objects, interacting with non-rigid objects remains
challenging for robots. In this work, we introduce velcro peeling as a
representative application for robotic manipulation of non-rigid objects in
complex environments. We present a method of learning force-based manipulation
from noisy and incomplete sensor inputs in partially observable environments by
modeling long term dependencies between measurements with a multi-step deep
recurrent network. We present experiments on a real robot to show the necessity
of modeling these long term dependencies and validate our approach in
simulation and robot experiments. Our results show that using tactile input
enables the robot to overcome geometric uncertainties present in the
environment with high fidelity in ~90% of all cases, outperforming the
baselines by a large margin.
