Touch2Insert: Zero-Shot Peg Insertion by Touching Intersections of Peg and Hole

2026-03-03

AI Summary

This work addresses the challenges of industrial connector insertion tasks, which are often hindered by visual occlusion, limited geometric generalization, and the difficulty of achieving sub-millimeter precision. The authors propose a zero-shot insertion method leveraging high-resolution tactile sensing: by making a single contact, the system reconstructs the cross-sectional geometry of both the plug and socket, then directly estimates their relative pose through shape registration—eliminating the need for task-specific training and enabling handling of arbitrary, previously unseen connector geometries. This approach overcomes the limitations of vision-based systems in occluded and generalizable scenarios, achieving sub-millimeter pose estimation accuracy in simulation and an average insertion success rate of 86.7% in real-world robotic experiments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Reliable insertion of industrial connectors remains a central challenge in robotics, requiring sub-millimeter precision under uncertainty and often without full visual access. Vision-based approaches struggle with occlusion and limited generalization, while learning-based policies frequently fail to transfer to unseen geometries. To address these limitations, we leverage tactile sensing, which captures local surface geometry at the point of contact and thus provides reliable information even under occlusion and across novel connector shapes. Building on this capability, we present \emph{Touch2Insert}, a tactile-based framework for arbitrary peg insertion. Our method reconstructs cross-sectional geometry from high-resolution tactile images and estimates the relative pose of the hole with respect to the peg in a zero-shot manner. By aligning reconstructed shapes through registration, the framework enables insertion from a single contact without task-specific training. To evaluate its performance, we conducted experiments with three diverse connectors in both simulation and real-robot settings. The results indicate that Touch2Insert achieved sub-millimeter pose estimation accuracy for all connectors in simulation, and attained an average success rate of 86.7\% on the real robot, thereby confirming the robustness and generalizability of tactile sensing for real-world robotic connector insertion.

Problem

• peg insertion
• tactile sensing
• zero-shot
• occlusion
• connector insertion

Innovation

• tactile sensing
• zero-shot insertion
• shape reconstruction
• pose estimation
• peg-in-hole