This paper gives a review of the recent developments in deep learning and unsupervised
feature learning for time-series problems. While these techniques have shown promise for …

Learning new motor tasks from physical interactions is an important goal for both robotics
and machine learning. However, when moving beyond basic skills, most monolithic machine …

Many motor skills in humanoid robotics can be learned using parametrized motor primitives
as done in imitation learning. However, most interesting motor learning problems are high …

We describe CST, an online algorithm for constructing skill trees from demonstration
trajectories. CST segments a demonstration trajectory into a chain of component skills …

The acquisition and self-improvement of novel motor skills is among the most important
problems in robotics. Motor primitives offer one of the most promising frameworks for the …

In this article, we present both novel learning algorithms and experiments using the
dynamical system MPs. As such, we describe this MP representation in a way that it is …

Probabilistic representations of movement primitives open important new possibilities for
machine learning in robotics. These representations are able to capture the variability of the …

Placing robotsoutside controlled conditions requires versatile movement representations
that allow robots to learn new tasks and adapt them to environmental changes. The …

Table tennis is a sufficiently complex motor task for studying complete skill learning systems.
It consists of several elementary motions and requires fast movements, accurate control, and …

Data driven methods for time series forecasting that quantify uncertainty open new important
possibilities for robot tasks with hard real time constraints, allowing the robot system to make …