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Related Experiment Videos

Stability-constrained incremental learning for robust ROV control with feasibility-guided data selection.

Bao Shi¹, Yongsheng Ou¹, Guoliang Zhao²

¹School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.

ISA Transactions

|July 8, 2026

Summary

This summary is machine-generated.

This study introduces a stability-constrained incremental learning framework for Remotely Operated Vehicles (ROVs). It improves control policy stability and data efficiency, even with limited demonstrations and disturbances.

Keywords:

Behavioral feasibility predictor Remotely operated vehicle motion control Stability-constrained incremental learning

Related Experiment Videos

Area of Science:

Robotics
Control Systems
Machine Learning

Background:

Remotely operated vehicles (ROVs) face challenges in complex aquatic environments with disturbances.
Acquiring high-quality demonstrations for ROV control is expensive and limited.
Improving control policy stability and data efficiency is difficult under these constraints.

Purpose of the Study:

To propose a novel stability-constrained incremental learning framework for high-level ROV control.
To enable stable and data-efficient policy improvement using limited demonstrations.
To enhance ROV robustness against unmodeled disturbances.

Main Methods:

Implemented an incremental learning scheme with a Lyapunov-inspired structural constraint for stability.
Developed a behavioral feasibility predictor to refine policies using sparse data.
Validated the framework through simulations and real-world ROV experiments.

Main Results:

The framework progressively improves the policy without violating stability structures.
Behavioral feasibility predictor enhances learning from sparse data and improves robustness.
Experimental results show improved tracking robustness and behavioral consistency.

Conclusions:

The proposed framework offers a stable and data-efficient approach for ROV control.
It effectively handles limited demonstrations and external disturbances.
This method advances learning-based control for underwater robotics.