Deep learning has exploded in the public consciousness, primarily as predictive and
analytical products suffuse our world, in the form of numerous human-centered smart-world …

Currently, the network traffic control systems are mainly composed of the Internet core and
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …

Reinforcement learning is an appealing approach for allowing robots to learn new tasks.
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …

Advances in deep learning over the last decade have led to a flurry of research in the
application of deep artificial neural networks to robotic systems, with at least 30 papers …

The recent advances in artificial intelligence have already begun to penetrate our daily lives.
Even though the development is still in its infancy, it has been shown that it can outperform …

Flexure-based compliant mechanisms are becoming increasingly promising in precision
engineering, robotics, and other applications due to the excellent advantages of no friction …

For robots to attain more general-purpose utility, grasping is a necessary skill to master.
Such general-purpose robots may use their perception abilities to visually identify grasps for …

High-precision trajectory tracking is fundamental in robotic manipulation. While industrial
robots address this through stiffness and high-performance hardware, compliant and cost …

In this review paper, we are interested in the models and algorithms that allow generic
simulation and control of a soft robot. First, we start with a quick overview of modeling …

Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …