  The rise in additive manufacturing comes with unique opportunities and
challenges. Rapid changes to part design and massive part customization
distinctive to 3D-Print (3DP) can be easily achieved. Customized parts that are
unique, yet exhibit similar features such as dental moulds, shoe insoles, or
engine vanes could be industrially manufactured with 3DP. However, the
opportunity for massive part customization comes with unique challenges for the
existing production paradigm of robotics applications, as the current robotics
paradigm for part identification and pose refinement is repetitive, where
data-driven and object-dependent approaches are often used. Thus, a bottleneck
exists in robotics applications for 3DP parts where massive customization is
involved, as it is difficult for feature-based deep learning approaches to
distinguish between similar parts such as shoe insoles belonging to different
people. As such, we propose a method that augments patterns on 3DP parts so
that grasping, part identification, and pose refinement can be executed in one
shot with a tactile gripper. We also experimentally evaluate our approach from
three perspectives, including real insertion tasks that mimic robotic sorting
and packing, and achieved excellent classification results, a high insertion
success rate of 95%, and a sub-millimeter pose refinement accuracy.
