Evolutionary robotics applies the selection, variation, and heredity principles of natural
evolution to the design of robots with embodied intelligence. It can be considered as a …

Reinforcement learning is about learning agent models that make the best sequential
decisions in unknown environments. In an unknown environment, the agent needs to …

Bayesian optimization is an effective methodology for the global optimization of functions
with expensive evaluations. It relies on querying a distribution over functions defined by a …

Abstract Entropy Search (ES) and Predictive Entropy Search (PES) are popular and
empirically successful Bayesian Optimization techniques. Both rely on a compelling …

Robots have transformed many industries, most notably manufacturing, and have the power
to deliver tremendous benefits to society, such as in search and rescue, disaster response …

Designing gaits and corresponding control policies is a key challenge in robot locomotion.
Even with a viable controller parametrization, finding near-optimal parameters can be …

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 …

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

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 …

Configuration knobs of database systems are essential to achieve high throughput and low
latency. Recently, automatic tuning systems using machine learning methods (ML) have …