7th Annual Learning for Dynamics & Control Conference
University of Michigan, Ann Arbor
June 4-6, 2025
7th Annual Learning for Dynamics & Control Conference
University of Michigan, Ann Arbor
June 4-6, 2025
The explosion of real-time data arising from devices that sense and control the physical world requires improving synergy in research 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 the 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 conference has been building a new community of people who think rigorously across the disciplines, ask new questions, and develop the foundations of this new scientific area.
March 27, 2025: The NSF student travel support application is now open! Use this link to review eligibility criteria and submit your application. The deadline for applications is April 21, 2025.
March 25, 2025: The oral presentations and poster sessions have been posted.
March 5, 2025: The tutorials have been posted.
Submission deadline: November 22, 2024 December 1, 2024, 11:59pm (AOE)
Author notification: End of February, 2025
Registration opens: March 7, 2025
Camera-ready deadline: April 2025
Tutorials day: June 4, 2025
Main conference: June 5-6, 2025
A "call for papers flyer" (pdf) is available here.
Please submit your paper via this EasyChair link, using the provided L4DC template.
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
Machine learning for reduced-order modeling and physics-constrained systems
Physical learning in dynamical and control systems applications in robotics, autonomy, biology, energy systems, transportation systems, cognitive systems, neuroscience, etc.
The conference is open to any topic on the interface between machine learning, control, and optimization; its primary goal is to address scientific and application challenges in real-time processes modeled by dynamical or control systems.
Papers are to be submitted via EasyChair here using the L4DC template here.
All papers must be written in English and be uploaded in PDF format.
Submissions are limited to 10 pages, including all figures, tables, conclusions, etc, with unlimited allowance for references. Acknowledgments do not count toward the page limit.
If the submitted paper includes an appendix, it should be within the 10-page limit.
Authors may include links to supplementary material (long proofs, additional experimental results, etc), but reviews and acceptance decisions will be based only on the submitted paper (10 pages). The links can be to Arxiv, a Google Drive file, a personal website, etc., but this supplementary material should not be attached to the submission PDF.
L4DC reviewing is single-blind.
All accepted papers will be presented as posters at the conference. A selected set of papers deemed particularly exceptional by the program committee will be presented as oral talks.
Please contact the conference organizers at l4dc-2025@umich.edu if you have any questions.
At the L4DC conference, we aim to create a safe, welcoming, and positive environment that facilitates exchanging ideas and forming professional connections. We expect all participants to be respectful towards each other. The conference will not tolerate harassment, discrimination, or personal attacks. Participants must stop any problematic behavior immediately. If you have concerns about problematic behavior, please notify the L4DC organizers at l4dc-2025@umich.edu. If you would like to notify the organizers anonymously, a form will be made available on this website before the conference.
It is important to note that problematic communication can occur even without an explicit intent to offend or harass. Hence, we ask that all participants be mindful of how their comments can be interpreted, and err on the side of caution during formal and informal interactions.
L4DC is interdisciplinary, bringing together researchers from control, robotics, machine learning, and optimization. One of the goals of L4DC is to build strong ties between these disciplines and enable active collaboration. Please consider taking on the constructive role of contributing to the community, instead of taking on a critical role to pinpoint flaws and weaknesses in the works of others during the conference. Focusing on weak points could cause others to retreat back to places where they feel comfortable, and instead of interdisciplinary interaction, we could end up with separate sub-clusters of people only interacting within their own fields. So please help us create a positive tone so that it is easy for the participants to leave their comfort zones, gain new perspectives, and form new collaborations.