  Wire harnesses are essential connecting components in manufacturing industry
but are challenging to be automated in industrial tasks such as bin picking.
They are long, flexible and tend to get entangled when randomly placed in a
bin. This makes it difficult for the robot to grasp a single one in dense
clutter. Besides, training or collecting data in simulation is challenging due
to the difficulties in modeling the combination of deformable and rigid
components for wire harnesses. In this work, instead of directly lifting wire
harnesses, we propose to grasp and extract the target following a circle-like
trajectory until it is untangled. We learn a policy from real-world data that
can infer grasps and separation actions from visual observation. Our policy
enables the robot to efficiently pick and separate entangled wire harnesses by
maximizing success rates and reducing execution time. To evaluate our policy,
we present a set of real-world experiments on picking wire harnesses. Our
policy achieves an overall 84.6% success rate compared with 49.2% in baseline.
We also evaluate the effectiveness of our policy under different clutter
scenarios using unseen types of wire harnesses. Results suggest that our
approach is feasible for handling wire harnesses in industrial bin picking.
