3rd Annual Learning for Dynamics & Control Conference

June 7 – 8, 2021 | ETH Zurich, Switzerland

Over the next decade, the biggest generator of data is expected to be devices that sense and control the physical world.

The explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning, control theory, and optimization. While control theory has been firmly rooted in the tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking the foundations of our discipline. From a machine learning perspective, one of the main challenges going forward is to go beyond pattern recognition and address problems in data-driven control and optimization of dynamical processes. Our overall goal is to create a new community of people who think rigorously across the disciplines, ask new questions, and develop the foundations of this new scientific area.

Following the success of the inaugural Learning for Dynamics and Control (L4DC) workshop at MIT in 2019, and L4DC 2020 in (virtual) Berkeley, we are very happy to welcome you to Europe and ETH Zurich.

For general questions and inquiries please contact the organizers at l4dc@ethz.ch.

Key Dates

Conference: June 7-8, 2021 Paper submission deadline: November 13, 2020 Author notification: March 1, 2021

Organizers

Call for Papers

We invite submissions of short papers addressing topics including:

  • Foundations of learning of dynamics models
  • System identification
  • Optimization for machine learning
  • Data-driven optimization for dynamical systems
  • Distributed learning over distributed systems
  • Reinforcement learning for physical systems
  • Safe reinforcement learning and safe adaptive control
  • Statistical learning for dynamical and control systems
  • Bridging model-based and learning-based dynamical and control systems
  • Physics-constrained learning
  • Physical learning in dynamical and control systems applications in robotics, autonomy, transportation systems, cognitive systems, neuroscience, etc.

While the conference is open to any topic on the interface between machine learning, control, optimization and related areas, its primary goal is to address scientific and application challenges in real-time physical processes modeled by dynamical or control systems.

All accepted papers will be presented as posters at this conference. A selected set of 10 papers deemed particularly exceptional by the program committee will be presented as 15 minute oral talks. At least one of each paper’s authors should be present at the conference to present the work. Accepted papers will be published electronically in the Proceedings of Machine Learning Research (PMLR). The authors of accepted papers will have the option of opting-out of the proceedings in favor of a 1-page extended abstract. The full paper reviewed will then be placed on the arXiv repository, but not indexed by the conference.

Dual Submissions

Submissions that are substantially similar to papers that have been previously published, accepted for publication, or submitted in parallel to other peer-reviewed conferences with proceedings or journals may not be submitted to L4DC.

Important Dates

  • Paper submission deadline: November 13, 2020.
  • Author notification: March 1, 2021.
  • Conference: June 7-8, 2021.

Submission instructions

  • Submissions are limited to 10 pages in PMLR format with unlimited allowance for references (Latex Style Sheet). In their submission, authors may point to an arXiv tech report if they wish.
  • L4DC reviewing is single blind.
  • We will begin accepting submissions via EasyChair on October 12, 2020.
  • The deadline for submissions is 5:00 PM EST on November 13, 2020.
  • Please contact the program chairs at l4dc@ethz.ch if you have any questions about the policy or technical issues with the submission process.

Confidentiality Policy

We maintain a policy of strict confidentiality for all conference submissions. You must notify your reviewers that they may not disclose or make inappropriate use of the content before publication.