Publications by Klas Jonas Alfred Kronander

Learning motions from demonstrations and rewards with time-invariant dynamical systems based policies

Aude Billard, Jonas Buchli, Klas Jonas Alfred Kronander, Joël Rey, Farbod Farshidian

2017

Learning Externally Modulated Dynamical Systems

Aude Billard, Nicolas Sommer, Klas Jonas Alfred Kronander

Dynamical Systems (DS) are often used to represent motion, with the advantage of being easy to learn from demonstrations. We present a method to modulate DS depending on an external signal, extending our previous work on Locally Modulated DS (LMDS, Kronand ...

2017

Passive Interaction Control With Dynamical Systems

Aude Billard, Klas Jonas Alfred Kronander

Autonomous dynamical systems (DS) has emerged as an extremely flexible and powerful method for modeling robotic tasks. Task execution of DS models is typically done in an open-loop manner in combination with standard low level controller, e.g. position con ...

2016

Stability Considerations for Variable Impedance Control

Aude Billard, Klas Jonas Alfred Kronander

Impedance control is a commonly used control architecture for robotic manipulation. For increased flexibility, the impedance can be programmed to vary during the task. This has important implications on the stability properties of the control system, which ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Control and Learning of Compliant Manipulation Skills

Klas Jonas Alfred Kronander

Humans demonstrate an impressive capability to manipulate fragile objects without damaging them, graciously controlling the force and position of hands or tools. Traditionally, robotics has favored position control over force control to produce fast, accur ...

EPFL2015

Incremental motion learning with locally modulated dynamical systems

Aude Billard, Klas Jonas Alfred Kronander

Dynamical Systems (DS) for robot motion modeling are a promising approach for efficient robot learning and control. Our focus in this paper is on autonomous dynamical systems, which represent a motion plan without dependency on time. We develop a method th ...

Elsevier2015

Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction

Aude Billard, Klas Jonas Alfred Kronander

Robot Learning from Demonstration (RLfD) has been identified as a key element for making robots useful in daily lives. A wide range of techniques has been proposed for deriving a task model from a set of demonstrations of the task. Most previous works use ...

Ieee Computer Soc2014

Learning to play minigolf: A dynamical system-based approach

Aude Billard, Seyed Mohammad Khansari Zadeh, Klas Jonas Alfred Kronander

A current trend in robotics is to define robot motions so that they can be easily adopted to situations beyond those for which the motion was originally designed. In this work, we show how the challenging task of playing minigolf can be efficiently tackled ...

Taylor & Francis Ltd2012

Online Learning of Varying Stiffness Through Physical Human-Robot Interaction

Aude Billard, Klas Jonas Alfred Kronander

Programming by Demonstration offers an intu- itive framework for teaching robots how to perform various tasks without having to preprogram them. It also offers an intuitive way to provide corrections and refine teaching during task execution. Previously, m ...

Ieee2012

Learning to Control Planar Hitting Motions in a Monigolf-like Task

Aude Billard, Seyed Mohammad Khansari Zadeh, Klas Jonas Alfred Kronander

A current trend in robotics is to define robot tasks using a combination of superimposed motion patterns. For maximum versatility of such motion patterns, they should be easily and efficiently adaptable for situations beyond those for which the motion was ...

IEEE2011