Subassembly to Full Assembly: Effective Assembly Sequence Planning through Graph-based Reinforcement Learning

TL;DR

This paper introduces a graph-based reinforcement learning framework for efficient assembly sequence planning, enabling robots to assemble complex structures by learning optimal action sequences with delayed rewards, validated through simulations and real-world tests.

Contribution

It presents a novel graph attention network approach with delayed reward strategy for scalable assembly sequence planning in robotics.

Findings

Outperforms baseline methods in simulation

Effective in real-world robotic assembly tasks

Delayed reward strategy improves learning efficiency

Abstract

This paper proposes an assembly sequence planning framework, named Subassembly to Assembly (S2A). The framework is designed to enable a robotic manipulator to assemble multiple parts in a prespecified structure by leveraging object manipulation actions. The primary technical challenge lies in the exponentially increasing complexity of identifying a feasible assembly sequence as the number of parts grows. To address this, we introduce a graph-based reinforcement learning approach, where a graph attention network is trained using a delayed reward assignment strategy. In this strategy, rewards are assigned only when an assembly action contributes to the successful completion of the assembly task. We validate the framework's performance through physics-based simulations, comparing it against various baselines to emphasize the significance of the proposed reward assignment approach. Additionally, we demonstrate the feasibility of deploying our framework in a real-world robotic assembly scenario.