Speaker (confirmed and tentative)

  • Abbeel, Pieter
  • Bagnell, Andrew
  • Dileep, George
  • Peters, Jan
  • Riedmiller, Martin
  • Schaal, Stefan
  • Stulp, Freek
  • Touissant, Marc
  • van der Smagt, Patrick
  • von Wichert, Georg


Robot learning combines the challenges of understanding, modeling and applying dynamical systems with task learning from rewards, through human robot interaction or from intrinsic motivation signals. While outstanding results using machine and deep learning have been generated in robot learning in the last years, current challenges in industrial applications are underrepresented. The goal of this workshop is to go beyond discussing potential industrial applications like in related past workshops.

The core elements of this event are industrial specific challenges, novel machine and deep learning approaches and unsolved complex problems. For that we will bring together leading experts from academia and industry to discuss current and future challenges in the application of learning mechanisms in industry. We try to have a problem-based look on robot learning: which methods would really help to advance robot learning applications and what are the demands from industry in the first place. Spiced with a discussion between various industrial as well as academic leaders this is unmatched by any classical conference session.


Dr. Kim Daniel Listmann, Head of ABB Future Labs Switzerland, kim.listmann@ch.abb.com, +41 79 325 9946

ABB Switzerland Ltd, Segelhofstrasse 1K, 5405 Baden-Dättwil, Switzerland.

Prof. Dr. Elmar Rueckert, Professor at the Institute for Robotics and Cognitive Systems, rueckert@rob.uniluebeck.de, +49 451 310 15 209

University of Luebeck, Ratzeburger Allee 160, 23562 Luebeck, Germany