A Dual-Channel Framework for Blind Perceptual Quality Assessment in Bilateral Teleoperation

Abstract

This paper proposes a perceptual no-reference (blind) haptic quality assessment framework for predicting the Quality of Experience (QoE) in teleoperation systems with force feedback. The proposed approach employs a deep neural network that combines semantic and distortion-based channels. The semantic network generates a semantic vector that characterizes the interaction between the robot and its environment. Meanwhile, the distortion network decomposes complex noise introduced by control algorithms and communication artifacts into artificial noise of known types. To train the proposed network, we also construct an augmented dataset for perceptual quality assessment in teleoperation based on the subjective experiments. The dataset augmentation and the model are validated with real-world teleoperation tasks. Our experimental results demonstrate that the performance of our No-Reference (NR) haptic quality assessment model is comparable to or surpasses that of commonly used Full-Reference (FR) methods, achieving Spearman’s Rank-Order Correlation scores above 0.85 for QoE prediction.