In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …

As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

In this survey, we present the current status on robots performing manipulation tasks that
require varying contact with the environment, such that the robot must either implicitly or …

A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

Most manipulation tasks can be decomposed into a sequence of phases, where the robot's
actions have different effects in each phase. The robot can perform actions to transition …

Inverse optimal control (IOC) assumes that demonstrations are the solution to an optimal
control problem with unknown underlying costs, and extracts parameters of these underlying …

Movement primitives are a well-established approach for encoding and executing
movements. While the primitives themselves have been extensively researched, the concept …

Tactile sensing is a fundamental component of object manipulation and tool handling skills.
With robots entering unstructured environments, tactile feedback also becomes an important …

One of the main challenges in autonomous manipulation is to generate appropriate multi-
modal reference trajectories that enable feedback controllers to compute control commands …

Abstract Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that
focuses on methods for solving off-nominal, or anomalous problems in autonomous …