Motivated by the challenges inherent in achieving high-accuracy tracking control of practical
6-degree-of-freedom (6-DOF) hydraulic robotic manipulators, we aim to conduct research on …

This paper introduces a novel approach to enhance the stability and efficiency of R-STDP in
the context of federated learning. The primary objective is to stabilize the unbounded growth …

Optimizing charging protocols is critical for reducing battery charging time and decelerating
battery degradation in applications such as electric vehicles. Recently, reinforcement …

Learning-based controllers show promising performances in robotic control tasks. However,
they still present potential safety risks due to the difficulty in ensuring satisfaction of complex …

Over the past years, self-driving mobile robots have captured the interest of researchers,
prompting exploration into their multifaceted implementation. They have the potential to …

Differentiable optimization has received a significant amount of attention due to its
foundational role in the domain of machine learning based on neural networks. The existing …

In today's society, robotics systems are being widely adopted in various fields of human
activity. Robots not only play a decisive role in modern intelligent industries, but are also …

Space exploration is often too hazardous for humans to perform, relying on autonomous
rovers and probes to safely operate. Sea navigation presents a similar challenge within a …

Offline reinforcement learning (RL) learns policies for solving sequential decision problems
directly from offline datasets. Most existing works focus on countering out-of-distribution …

Co-simulation is a process of combining several simulation environments simultaneously.
This approach is critical in requirements to model the interactions of multiple physical …