Beyond Wave Variables: A Data-Driven Ensemble Approach for Enhanced Teleoperation Transparency and Stability
Nour Mitiche, Farid Ferguene, Mourad Oussalah

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTeleoperation and Haptic Systems · Network Time Synchronization Technologies · Tactile and Sensory Interactions
