The potential of neuromorphic computing to bring about revolutionary advancements in
multiple disciplines, such as artificial intelligence (AI), robotics, neurology, and cognitive …

The increasing demand for applications in competitive fields, such as assisted living and
aerial robots, drives contemporary research into the development, implementation and …

Neural networks implemented in memristor-based hardware can provide fast and efficient in-
memory computation, but traditional learning methods such as error back-propagation are …

Spiking neural networks (SNNs) have revolutionized neural learning and are making
remarkable strides in image analysis and robot control tasks with ultra-low power …

In robotics, Spiking Neural Networks (SNNs) are increasingly recognized for their largely-
unrealized potential energy efficiency and low latency particularly when implemented on …

In this paper, the human-like motion issue for anthropomorphic arms is further discussed. An
Intelligent Human-like Motion Planner (IHMP) consisting of Movement Primitive (MP) …

Deep reinforcement learning is used in various domains, but usually under the assumption
that the environment has stationary conditions like transitions and state distributions. When …

An enhanced active reinforcement learning technique has been proposed to enable
autonomous robots to operate and execute tasks in industrial automation. This approach …

Abstract Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural
Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their …