Task-parameterized models of movements aim at automatically adapting movements to new
situations encountered by a robot. The task parameters can, for example, take the form of …

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

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 …

Task-parameterized models provide a representation of movement/behavior that can adapt
to a set of task parameters describing the current situation encountered by the robot, such as …

This article describes our open-source software for predicting the intention of a user
physically interacting with the humanoid robot iCub. Our goal is to allow the robot to infer the …

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 …

Simultaneous mastering of multiple tasks during motion generation is challenging.
Traditional null-space based approaches for redundant robots implement a strict …

Whole-Body Control methods offer the potential to execute several tasks on highly
redundant robots, such as humanoids. Unfortunately, task combinations often result in …

The success of robots in real-world environments is largely dependent on their ability to
interact with both humans and said environment. The FP7 EU project CoDyCo focused on …