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

TL;DR

Touch2Insert introduces a tactile-based, zero-shot framework for peg insertion that accurately estimates pose and geometry from a single contact, overcoming visual occlusion and generalizing to unseen shapes in robotics.

Contribution

The paper presents a novel tactile sensing approach that reconstructs geometry and estimates pose without task-specific training, enabling reliable peg insertion across diverse connectors.

Findings

Achieved sub-millimeter pose accuracy in simulation.

Attained 86.7% success rate on real robots.

Demonstrated robustness and generalization of tactile sensing.

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.