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

TL;DR

This paper introduces a tactile-based method for simultaneous grasping, part identification, and pose refinement of 3D printed parts, addressing challenges in rapid customization and feature-based deep learning limitations.

Contribution

It proposes a novel pattern augmentation technique enabling one-shot tactile recognition and pose refinement for customized 3D printed parts.

Findings

Achieved 95% success rate in insertion tasks

Demonstrated sub-millimeter pose refinement accuracy

Validated effectiveness in robotic sorting and packing scenarios

Abstract

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.