Biological systems, including human beings, have the innate ability to perform complex
tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to …

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds
significant promise for capturing expert motor skills through efficient imitation, facilitating …

Movement Primitives (MPs) are a well-known concept to represent and generate modular
trajectories. MPs can be broadly categorized into two types:(a) dynamics-based approaches …

The development and use of intelligent transportation systems as an emerging trend in the
application of computational intelligence within the concept of internet of vehicles (IoV) is …

Dynamic movement primitives (DMPs) have been widely applied in robot motion planning
and control. However, in some special cases, original discrete DMP fails to generalize …

Abstract The Multi-Layer Perceptron (MLP) has been applied into many real-world problems
as one of the most extensively used Neural Networks (NNs). It often suffers from local …

Just like humans, robots can improve their performance by practicing, ie by performing the
desired behavior many times and updating the underlying skill representation using the …

Metaheuristic optimization algorithms play a crucial role in optimization problems. However,
the traditional identification methods have the following problems:(1) difficulties in nonlinear …

Learning from humans allows nonexperts to program robots with ease, lowering the
resources required to build complex robotic solutions. Nevertheless, such data-driven …

Breast cancer is the most common type of cancer worldwide. A robotic system performing
autonomous breast palpation can make a significant impact on the related health sector …