Beyond Wave Variables: A Data-Driven Ensemble Approach for Enhanced Teleoperation Transparency and Stability

TL;DR

This paper introduces a data-driven ensemble method using advanced sequence models to improve transparency and stability in teleoperation systems affected by communication delays and noise.

Contribution

It replaces traditional wave-variable transforms with a hybrid ensemble of models optimized via Optuna, enhancing teleoperation performance.

Findings

Achieves transparency comparable to wave-variable systems under delays and noise.

Ensures stability through passivity constraints.

Demonstrates effectiveness with experimental validation.

Abstract

Time delays in communication channels present significant challenges for bilateral teleoperation systems, affecting both transparency and stability. Although traditional wave variable-based methods for a four-channel architecture ensure stability via passivity, they remain vulnerable to wave reflections and disturbances like variable delays and environmental noise. This article presents a data-driven hybrid framework that replaces the conventional wave-variable transform with an ensemble of three advanced sequence models, each optimized separately via the state-of-the-art Optuna optimizer, and combined through a stacking meta-learner. The base predictors include an LSTM augmented with Prophet for trend correction, an LSTM-based feature extractor paired with clustering and a random forest for improved regression, and a CNN-LSTM model for localized and long-term dynamics. Experimental validation was performed in Python using data generated from the baseline system implemented in MATLAB/Simulink. The results show that our optimized ensemble achieves a transparency comparable to the baseline wave-variable system under varying delays and noise, while ensuring stability through passivity constraints.