  Robotic peg-in-hole assembly represents a critical area of investigation in
robotic automation. The fusion of reinforcement learning (RL) and deep neural
networks (DNNs) has yielded remarkable breakthroughs in this field. However,
existing RL-based methods grapple with delivering optimal performance under the
unique environmental and mission constraints of fusion applications. As a
result, we propose an inventively designed RL-based approach. In contrast to
alternative methods, our focus centers on enhancing the DNN architecture rather
than the RL model. Our strategy receives and integrates data from the RGB
camera and force/torque (F/T) sensor, training the agent to execute the
peg-in-hole assembly task in a manner akin to human hand-eye coordination. All
training and experimentation unfold within a realistic environment, and
empirical outcomes demonstrate that this multi-sensor fusion approach excels in
rigid peg-in-hole assembly tasks, surpassing the repeatable accuracy of the
robotic arm utilized--0.1 mm--in uncertain and unstable conditions.
