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The rise in additive manufacturing comes with unique opportunities andchallenges. Rapid changes to part design and massive part customizationdistinctive to 3D-Print (3DP) can be easily achieved. Customized parts that areunique, yet exhibit similar features such as dental moulds, shoe insoles, orengine vanes could be industrially manufactured with 3DP. However, theopportunity for massive part customization comes with unique challenges for theexisting production paradigm of robotics applications, as the current roboticsparadigm for part identification and pose refinement is repetitive, wheredata-driven and object-dependent approaches are often used. Thus, a bottleneckexists in robotics applications for 3DP parts where massive customization isinvolved, as it is difficult for feature-based deep learning approaches todistinguish between similar parts such as shoe insoles belonging to differentpeople. As such, we propose a method that augments patterns on 3DP parts sothat grasping, part identification, and pose refinement can be executed in oneshot with a tactile gripper. We also experimentally evaluate our approach fromthree perspectives, including real insertion tasks that mimic robotic sortingand packing, and achieved excellent classification results, a high insertionsuccess rate of 95%, and a sub-millimeter pose refinement accuracy.

Grasping, Part Identification, and Pose Refinement in One Shot with a Tactile Gripper