Call For Papers

We welcome submissions of extended abstract (max. 2 pages) in PDF format and prepared using the standard template for R:SS. Submissions will be made via TBD.

Each abstract will be peer-reviewed and selected based on their originality, relevance, technical clarity, and presentation. The accepted abstracts will be made available on the workshop's website (upon authors agreement). Accepted contributions will be presented during a virtual poster session, whose format will follow the general format of R:SS’26.

Topics of interest

This workshop will explore how geometric and physics-informed structures can support the design of more reliable, interpretable, and data-efficient methods for robot learning, planning, and control. In particular, we aim to bring together researchers working on geometric robotics, robot learning, control, applied mathematics, and physics-informed machine learning. 

The topics of interest include (but not limited to) the following:

•     Riemannian geometry in robotics

•     Lie groups, symmetries, and invariances for learning and control

•     Geometric formulations of Lagrangian and Hamiltonian dynamics

•     Physics-inspired learning models and learning-based control

•     Structure-preserving model reduction for robotic systems

•     Geometry-aware motion generation, planning, and decision making

•     Robust and data-efficient learning on non-Euclidean spaces

•     Scaling geometric constraints for real-time robot control

•     Geometric structure in foundation models and generative policies for robotics