With the ever-increasing complications of current assistive systems, it is becoming imperative that these systems progress their own intelligence to reduce their reliance on human operators. The field of robotics is changing our daily life with its applications ranging from robotic manipulators in factory assembly lines to unmanned systems in transportation. Due to the inherent complexity in robotics, an advanced learning methodology is a key to self-learning and fast adaptation to disturbances. Deep Learning (DL) architectures and algorithms provide wide solutions and help in assistive systems. DL aims to provide culturing feature hierarchies with features learned from upper levels of the hierarchy which in turn are shaped by the alignment of lower-level features. Inevitably, learning at manifold levels enables a system to learn compound functions, mapping the input-to-output directly from data available, without being dependent on human-crafted features. In other words, DL can be the ideal formulae for solutions in assistive systems.

Intelligent control theory is an active field of research that brings together artificial intelligence and automatic control to solve complex control problems, such as robotics. This class of control techniques is composed of neural network control, fuzzy logic control, neuro-fuzzy control, evolutionary computation, swarm algorithms, self-organizing systems, soft computing, machine learning, and intelligent agents-based control, to name a few. These strategies are very useful when no prior mathematical model is available for the system to be controlled.

This Special Issues aims to collate original research and review articles describing advances in this field.

Potential topics include but are not limited to the following:

• Assistive and medical technologies
• Multi-agent learning in assistive systems
• Cooperating swarm robotics in assistive systems
• Intelligent control of robotic manipulators in assistive systems
• Intelligent autonomous systems (unmanned surface/underwater/aerial vehicles)
• Goal-based skill learning in assistive systems
• Intelligent multiagent control systems in assistive systems
• Intelligent modeling and identification in assistive systems
• Hybridization techniques in intelligent control assistive systems