The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

Abstract Models are among the most essential tools in robotics, such as kinematics and
dynamics models of the robot's own body and controllable external objects. It is widely …

The last decade witnessed increasingly rapid progress in self‐driving vehicle technology,
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …

Abstract Model-based reinforcement learning (RL) algorithms can attain excellent sample
efficiency, but often lag behind the best model-free algorithms in terms of asymptotic …

Reinforcement Learning (RL) algorithms have found limited success beyond simulated
applications, and one main reason is the absence of safety guarantees during the learning …

This paper presents a method to learn discrete robot motions from a set of demonstrations.
We model a motion as a nonlinear autonomous (ie, time-invariant) dynamical system (DS) …

This paper presents a Robust Constrained Learning-based Nonlinear Model Predictive
Control (RC-LB-NMPC) algorithm for path-tracking in off-road terrain. For mobile robots …

We apply an online optimization process based on machine learning to the production of
Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation …

We develop a nonparametric method for inferring the universal neutron star (NS) equation of
state (EOS) from gravitational wave (GW) observations. Many different possible realizations …

Air quality has drawn much attention in the recent years because it seriously affects people's
health. Nowadays, monitoring stations in a city can provide real-time air quality, but people …