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 …

In the recent years, the Internet of Things (IoT) services has been increasingly applied to
promote the quality of the human life and this trend is predicted to stretch for into future. With …

Proximal policy optimization (PPO) is one of the most successful deep reinforcement
learning methods, achieving state-of-the-art performance across a wide range of …

We consider Bayesian optimization of objective functions of the form $\rho [F (x, W)] $, where
$ F $ is a black-box expensive-to-evaluate function and $\rho $ denotes either the VaR or …

Online field experiments are the gold-standard way of evaluating changes to real-world
interactive machine learning systems. Yet our ability to explore complex, multi-dimensional …

We consider the problem of generating a time-optimal quadrotor trajectory for highly
maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the …

Featured Application The final product is an algorithm that allows a simulated robot to learn
sequences of poses (single configurations of its joints) in order to learn to walk (and …

The generation of simulation data from physical models trying to mimic parts of the real-
world process as accurately as possible has received much attention in industry during the …

Legged robots are becoming increasingly agile in exhibiting dynamic behaviors such as
running and jumping. Usually, such behaviors are either optimized and engineered offline …

Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In
this work, we focus on robotics problems with a budget of only 10-20 trials. This is a very …