Second Max Planck ETH Workshop on Learning Control
8/9 Febuary 2018, Zurich, Switzerland

After a successful first edition in 2015, we are pleased to announce the second workshop on Learning Control within the Max Planck ETH Center for Learning Systems. The workshop will take place February 8-9 2018 at ETH Zurich. We cordially invite all researchers from ETH Zürich and MPI-IS interested in the area of Learning Control to participate and actively contribute to this workshop.

Aims and Scope

The workshop aims at bringing together researchers from ETH Zürich and MPI-IS working in the area of Learning Control in order to create and grow a community of interest within the Max Planck ETH Center for Learning Systems. The workshop will provide a platform to exchange ideas, present current research, discuss challenges for Learning Control, and initiate future research collaborations within the Center.

The topic and scope of this workshop is Learning Control. Albeit not uniquely defined, we understand Learning Control as the rather broad research area that lies at the intersection of Machine Learning and Automatic Control. This includes, but is not limited to reinforcement learning, machine learning for control, data-driven control, adaptive control, dual control, online learning, active learning for control, model learning, and applications of learning control.

The workshop is open to all researchers from MPI-IS and ETH. Nevertheless, applications of external researchers are possible, though priority will be given to researchers from MPI-IS and ETH (depending on available places). We seek to create an informal atmosphere to foster open discussions and exchange of ideas.


All participants are asked to submit a maximum one page abstract detailing their research interests. In addition to presenting research results in the area of Learning Control, we encourage participants to also include open research questions, early ideas, or any topic that can lead to interesting and fruitful discussions in this exciting area.

Submissions will be briefly reviewed to ensure adequacy with the scope of the workshop. A few submissions will be selected for short plenary presentation. All other accepted submissions will be presented in interactive poster format.


In addition to interactive presentations and short talks, there will be invited keynote talks, a panel discussion, as well as social events (dinner on Thursday evening) with ample room for discussions and informal interactions.

We are excited to have the following invited speakers:

  • Thomas Schön, Professor at Department of Information Technology, Uppsala University, Sweden
  • John Lygeros, Professor at Automatic Control Laboratory, ETH Zurich, Switzerland
  • Nicolas Heess , Deepmind

Costs & logistics

Accommodation and travel costs for participants from MPI-IS (CLS members, associated members, students / co-workers of their groups) are covered by the Max Planck ETH Center for Learning Systems.

The following hotels have been booked.

Meals (lunch, dinner, coffee breaks) are offered for all participants.

Workshop location

All workshop activities, including registration on the first day, take place at ETH Zurich, CAB building, lecture room CAB G 51.
ETH Zurich
Department of Computer Science
CAB Building
Universitätsstrasse 6
8092 Zurich

Directions from Zurich main station to CAB building:
Tram no. 6 (direction Zoo) or Tram no. 10 (direction Airport) to stop „ETH/UniversitätsSpital“.

The dinner on Thursday evening Feb 8, 2018 will be at Hotel Uto Kulm on top of Uetliberg (detailed information will be provided during the workshop).


  • November 2017 - Registration/abstract submission opens (registration through website below)
  • 15th December 2017 - Registration and abstract submission deadline
  • 23rd December 2017 - Notification of acceptance
  • 8 February 2018 (around noon) - Workshop starts
  • 9 February 2018 (early evening) - Workshop ends


Event registration is now closed



Sebastian Trimpe

MPI-IS, Autonomous Motion Department

Melanie Zeilinger

ETH Zürich, Institute for Dynamic Systems and Control

Georg Martius

MPI-IS, Autonomous Learning Group

Magdalena Seebauer

Learning Systems Scientific Coordination