As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Robotic equipment has been playing a central role since the proposal of smart
manufacturing. Since the beginning of the first integration of industrial robots into production …

Elastic properties of classical bulk materials can hardly be changed or adjusted in operando,
while such tunable elasticity is highly desired for robots and smart machinery. Although …

Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …

We demonstrate that models trained only in simulation can be used to solve a manipulation
problem of unprecedented complexity on a real robot. This is made possible by two key …

Learned locomotion policies can rapidly adapt to diverse environments similar to those
experienced during training but lack a mechanism for fast tuning when they fail in an out-of …

Since the late 2019, the COVID-19 pandemic has been spread all around the world. The
pandemic is a critical challenge to the health and safety of the general public, the medical …

The intrinsic compliance of soft robots provides safety, a natural adaptation to its
environment, allows to absorb shocks, and protects them against mechanical impacts …

Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …

Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …