A robust and compliant robotic assembly control strategy for batch precision assembly task with uncertain fit types and fit amounts

TL;DR

This paper presents a reinforcement learning-based control strategy for robotic batch precision assembly that handles uncertain fit types and amounts, improving success rates and force compliance in high-precision tasks.

Contribution

It introduces a novel FVFC-MTRL framework that decomposes assembly tasks, learns multiple compliance strategies, and integrates them into a robust control policy for uncertain fits.

Findings

Successfully constructs a robust control strategy for uncertain fit types and amounts.

Significantly improves training efficiency with the MTRL framework.

Achieves higher success rates and better force compliance than existing methods.

Abstract

In some high-precision industrial applications, robots are deployed to perform precision assembly tasks on mass batches of manufactured pegs and holes. If the peg and hole are designed with transition fit, machining errors may lead to either a clearance or an interference fit for a specific pair of components, with uncertain fit amounts. This paper focuses on the robotic batch precision assembly task involving components with uncertain fit types and fit amounts, and proposes an efficient methodology to construct the robust and compliant assembly control strategy. Specifically, the batch precision assembly task is decomposed into multiple deterministic subtasks, and a force-vision fusion controller-driven reinforcement learning method and a multi-task reinforcement learning training method (FVFC-MTRL) are proposed to jointly learn multiple compliance control strategies for these subtasks. Subsequently, the multi-teacher policy distillation approach is designed to integrate multiple trained strategies into a unified student network, thereby establishing a robust control strategy. Real-world experiments demonstrate that the proposed method successfully constructs the robust control strategy for high-precision assembly task with different fit types and fit amounts. Moreover, the MTRL framework significantly improves training efficiency, and the final developed control strategy achieves superior force compliance and higher success rate compared with many existing methods.