Work

Deep Reinforcement Learning-Based Water Jet Control for Robotic Manipulators Using an Improved Experience Replay Mechanism

Year: 2026

Type: article

Abstract: Existing robotic water jet control methods are limited by fixed spray configurations and low adaptability to complex or dynamic environments. These constraints hinder precise targeting in three-dimensional spaces. To overcome this, we propose a reinforcement learning-based water jet control framework that achieves accurate targeting without pose or angle restrictions. Specifically, we introduce Goal-Priority Hindsight Experience Replay (GPHER), a replay strategy that integrates the principles of Hindsight Experience Replay (HER), Prioritized Experience Replay (PER), and curriculum learning. GPHER dynamically adjusts sampling priorities based on goal-space distance, guiding training from simple to complex goals. Combined with Truncated Quantile Critics (TQCs), this approach accelerates convergence and enhances success rates. Both simulation and real-world experiments validate the robustness and adaptability of the proposed method, demonstrating its effectiveness for real-time robotic fluid control.

Source: Applied Sciences

DOI: 10.3390/app16063099

Authors: Rong Zhang, Jianjun Qin, Luyang Wang, Guotong Li

Institution: Beijing University of Civil Engineering and Architecture

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Beijing Jiaotong University, Beijing University of Civil Engineering and Architecture