Contact Pose Identification for Peg-in-Hole Assembly under Uncertainties

2021/01/29 by Shiyu Jin, Jin, Shiyu, Xinghao Zhu +5 · 4 citations
Computer Science · Engineering · Physics and Astronomy · #Adhesion, Friction, and Surface Interactions #FOS: Computer and information sciences #Force Microscopy Techniques and Applications #Robot Manipulation and Learning #Robotics (cs.RO) #cs.RO

paper · pdf · doi:10.48550/arxiv.2101.12467

arxiv created 2021/01/29 · openalex publication_date 2021/01/29 · arxiv updated 2021/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Peg-in-hole assembly is a challenging contact-rich manipulation task. There is no general solution to identify the relative position and orientation between the peg and the hole. In this paper, we propose a novel method to classify the contact poses based on a sequence of contact measurements. When the peg contacts the hole with pose uncertainties, a tilt-then-rotate strategy is applied, and the contacts are measured as a group of patterns to encode the contact pose. A convolutional neural network (CNN) is trained to classify the contact poses according to the patterns. In the end, an admittance controller guides the peg towards the error direction and finishes the peg-in-hole assembly. Simulations and experiments are provided to show that the proposed method can be applied to the peg-in-hole assembly of different geometries. We also demonstrate the ability to alleviate the sim-to-real gap.

Citations

• ImageNet classification with deep convolutional neural networks
• Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data

Cited by

• Accurate Pose Estimation Using Contact Manifold Sampling for Safe Peg-in-Hole Insertion of Complex Geometries
• Robust Peg-in-Hole Assembly under Uncertainties via Compliant and Interactive Contact-Rich Manipulation
• Learning the Contact Manifold for Accurate Pose Estimation During Peg-in-Hole Insertion of Complex Geometries
• A General Peg-in-Hole Assembly Policy Based on Domain Randomized Reinforcement Learning

Related

• Learning the Contact Manifold for Accurate Pose Estimation During Peg-in-Hole Insertion of Complex Geometries
• Quickly Inserting Pegs into Uncertain Holes using Multi-view Images and Deep Network Trained on Synthetic Data
• Robust Peg-in-Hole Assembly under Uncertainties via Compliant and Interactive Contact-Rich Manipulation
• Accurate Pose Estimation Using Contact Manifold Sampling for Safe Peg-in-Hole Insertion of Complex Geometries
• In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components