# IPT-DCD: Interpolation Predictor for Teleoperation Under Dynamic Communication Delay Using Deep Learning Approach

## TL;DR

This paper introduces IPT-DCD, a deep learning-based predictor that improves teleoperation systems by handling dynamic communication delays more effectively.

## Contribution

The novel contribution is the development of an LSTM-based interpolation predictor that enhances robustness in unstable teleoperation environments.

## Key findings

- IPT-DCD outperforms baseline models in handling large communication delay outliers.
- The Backward Shifting and Interpolation technique restores signal consistency effectively.
- The model generates real-time steering commands using a many-to-many time series structure.

## Abstract

Teleoperation systems experience degraded control stability and safety due to dynamic communication delays. This study proposes an Interpolation Predictor for Teleoperation under Dynamic Communication Delay (IPT-DCD), a predictor that reconstructs asynchronously received control commands via interpolation and predicts future commands using an encoder–decoder LSTM architecture. To restore the temporal consistency of delayed signals, a signal preprocessing technique called the Backward Shifting and Interpolation (BSI) was applied, enabling the transformation of received data into an undelayed and uniformly sampled format. As a result, the proposed model was capable of generating real-time steering command outputs through a many-to-many time series structure. Furthermore, to evaluate its effectiveness, IPT-DCD was experimentally compared with a baseline model, a Predictor for Teleoperation under Dynamic Communication Delay (PT-DCD). The results reveal that IPT-DCD exhibits significantly greater robustness to large communication delay outliers than the baseline, highlighting its effectiveness in dynamic and unstable teleoperation environments.