Robust Peg-in-Hole Assembly under Uncertainties via Compliant and Interactive Contact-Rich Manipulation

TL;DR

This paper introduces a contact-rich manipulation system that uses compliance and environmental contact to robustly perform peg-in-hole assembly under uncertainties without relying on precise perception, applicable across diverse scenarios.

Contribution

It presents a formal approach to constructing manipulation funnels that absorb uncertainties, enabling robust peg-in-hole assembly without prior precise sensing or learning.

Findings

System successfully localizes target holes through contact interactions.

Robust insertion achieved across various shapes, sizes, and materials.

Validated on real-world tasks with high success rates.

Abstract

Robust and adaptive robotic peg-in-hole assembly under tight tolerances is critical to various industrial applications. However, it remains an open challenge due to perceptual and physical uncertainties from contact-rich interactions that easily exceed the allowed clearance. In this paper, we study how to leverage contact between the peg and its matching hole to eliminate uncertainties in the assembly process under unstructured settings. By examining the role of compliance under contact constraints, we present a manipulation system that plans collision-inclusive interactions for the peg to 1) iteratively identify its task environment to localize the target hole and 2) exploit environmental contact constraints to refine insertion motions into the target hole without relying on precise perception, enabling a robust solution to peg-in-hole assembly. By conceptualizing the above process as the composition of funneling in different state spaces, we present a formal approach to constructing manipulation funnels as an uncertainty-absorbing paradigm for peg-in-hole assembly. The proposed system effectively generalizes across diverse peg-in-hole scenarios across varying scales, shapes, and materials in a learning-free manner. Extensive experiments on a NIST Assembly Task Board (ATB) and additional challenging scenarios validate its robustness in real-world applications.