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.

Submissions

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.

Presentation/Publication

• All accepted papers will be presented as posters at this conference. A selected set of papers deemed particularly exceptional by the program committee will be presented as 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).

Instructions

• Submissions are limited to 10 pages in PMLR format with unlimited allowance for references. (Latex Template)
• L4DC reviewing is single blind.

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 30, 2021, 5:00 PM EST December 7, 2021, 5:00 PM EST
• Author notification: March 1, 2022.
• Final Paper Upload Deadline: April 5, 2022. Authors will be directly contacted by e-mail regarding the submission procedure.
• General Registration: April 10 – June 15, 2022.
• Conference: June 23-24, 2022.

Committees

Organizing committee

• Mykel Kochenderfer (Stanford University)
• Jeanette Bohg (Stanford University)
• Mac Schwager (Stanford University)
• Negar Mehr (UIUC)
• Rika Antonova (Stanford University)
• Roya Firoozi (Stanford University)
• Esen Yel (Stanford University) 

Steering committee

• Ali Jadbabaei (MIT)
• John Lygeros (ETH Zurich)
• George Pappas (Penn)
• Pablo Parillo (MIT)
• Ben Recht (UC Berkeley)
• Claire Tomlin (UC Berkeley)
• Melanie Zeilinger (ETH Zurich)

Contact

All questions about submissions should be emailed to l4dc-organizers@lists.stanford.edu.