Grasping, Part Identification, and Pose Refinement in One Shot with a Tactile Gripper
Joyce Xin-Yan Lim, Quang-Cuong Pham

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobot Manipulation and Learning · Tactile and Sensory Interactions · Human Pose and Action Recognition
