A Knowledge-Augmented Probabilistic Decision Model for Human-Robot Collaborative Assembly Under Multiple Uncertainties

Abstract

Human-robot collaborative assembly (HRCA) for mass customization frequently exhibits multiple uncertainties in assembly sequences, operator behaviors, and robot states. Existing research often addresses these uncertainties in isolation and lacks a knowledge-aware framework, thereby limiting context-aware adaptation and causing a decoupling between planning and execution. This study proposes a knowledge-augmented probabilistic decision model to synergistically handle these multiple uncertainties in HRCA. Firstly, a unified knowledge representation method integrating human-robot-process-component semantics is proposed using a pre-trained language model. Secondly, a knowledge-augmented partially observable Markov decision process model is established that embeds the structured knowledge into a probabilistic decision framework. The model quantifies human-robot uncertainties through online action prediction probabilities and target part pose scores. Based on the quantification, it enables online policy generation. Thirdly, an execution-level grasp adjustment strategy combining 2D detection and pose scores is proposed to guide end-effector corrections. Experimental results on reducer assembly tasks show that the proposed model achieves an efficiency improvement of 14.40% compared to the manually constrained partially observable Markov decision process baseline. The proposed model offers a robust solution for collaborative decisions in HRCA under multiple uncertainties.