  Gear assembly is an essential but challenging task in industrial automation.
This paper presents a novel two-stage approach for achieving high-precision and
flexible gear assembly. The proposed approach integrates YOLO to coarsely
localize the workpiece in a searching phase and deep reinforcement learning
(DRL) to complete the insertion. Specifically, DRL addresses the challenge of
partial visibility when the on-wrist camera is too close to the workpiece.
Additionally, force feedback is used to smoothly transit the process from the
first phase to the second phase. To reduce the data collection effort for
training deep neural networks, we use synthetic RGB images for training YOLO
and construct an offline interaction environment leveraging sampled real-world
data for training DRL agents. We evaluate the proposed approach in a gear
assembly experiment with a precision tolerance of 0.3mm. The results show that
our method can robustly and efficiently complete searching and insertion from
arbitrary positions within an average of 15 seconds.
