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.

翻译：增材制造的兴起带来了独特的机遇与挑战。3D打印（3DP）特有的零件设计快速变更和大规模定制可轻松实现。诸如牙模、鞋垫或发动机叶片等虽具独特性却拥有相似特征的定制化零件，可通过3DP实现工业化制造。然而，大规模零件定制的机遇为现有机器人应用生产范式带来了独特挑战——当前识别和位姿精调的机器人范式具有重复性特征，常依赖数据驱动和基于对象的方法。因此，涉及大规模定制的3DP零件机器人应用存在瓶颈，基于特征的深度学习方法难以区分不同人群的鞋垫等相似零件。为此，我们提出一种在3DP零件表面增强图案的方法，使触觉夹爪能一次性完成抓取、零件识别与位姿精调。我们通过模拟机器人分拣与打包的真实插入任务等三个维度实验验证了该方法，获得了优异的分类结果、95%的高插入成功率以及亚毫米级的位姿精调精度。