Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …

Artificial intelligence and mechanical engineering are two mature fields of science that
intersect more and more often. Computer-aided mechanical analysis tools, including …

Selecting the right tuning parameters for algorithms is a pravelent problem in machine
learning that can significantly affect the performance of algorithms. Data-efficient …

One of the most fundamental problems when designing controllers for dynamic systems is
the tuning of the controller parameters. Typically, a model of the system is used to obtain an …

We present Residual Policy Learning (RPL): a simple method for improving
nondifferentiable policies using model-free deep reinforcement learning. RPL thrives in …

In practice, the parameters of control policies are often tuned manually. This is time-
consuming and frustrating. Reinforcement learning is a promising alternative that aims to …

Bayesian optimization (BO) is proposed for automatic learning of optimal controller
parameters from experimental data. A probabilistic description (a Gaussian process) is used …

In robotics, optimizing controller parameters under safety constraints is an important
challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and …

Real-world robots are becoming increasingly complex and commonly act in poorly
understood environments where it is extremely challenging to model or learn their true …

Many black-box optimization tasks arising in high-stakes applications require risk-averse
decisions. The standard Bayesian optimization (BO) paradigm, however, optimizes the …