This chapter presents an overview of learning approaches for the acquisition of controllers
and movement skills in humanoid robots. The term learning control refers to the process of …

Bimanual operations in humanoids offer the possibility to carry out more than one
manipulation task at the same time, which in turn introduces the problem of task …

Probability distributions are key components of many learning from demonstration (LfD)
approaches, with the spaces chosen to represent tasks playing a central role. Although the …

One of the key problems in planning and control of redundant robots is the fast generation of
controls when multiple tasks and constraints need to be satisfied. In the literature, this …

When learning skills from demonstrations, one is often required to think in advance about
the appropriate task representation (usually in either operational or configuration space). We …

Movement prioritization is a common approach to combine controllers of different tasks for
redundant robots, where each task is assigned a priority. The priorities of the tasks are often …

In emerging manufacturing facilities, robots must enhance their flexibility. They are expected
to perform complex jobs, showing different behaviors on the need, all within unstructured …

Generating complex movements in redundant robots like humanoids is usually done by
means of multi-task controllers based on quadratic programming, where a multitude of tasks …

Multi-task prioritized controllers are able to generate complex robot behaviors that
concurrently satisfy several tasks and constraints. To perform, they often require a human …

Traditional industrial applications involve robots with limited mobility. Consequently,
interaction (eg manipulation) was treated separately from whole-body posture (eg …