10 papers
Exploiting Chordal Sparsity for Globally Optimal Estimation with Factor Graphs
Avinash Subramanian, Connor Holmes, Timothy D. Barfoot +2
Robust and efficient state estimation is crucial for perception, navigation, and control in robotics. State estimation problems are conveniently modeled using the factor-graph fram…
KSOS-BO: Improving Sampling in Bayesian Optimization via Kernel Sum of Squares
Buqing Ou, Frederike Dümbgen
Bayesian Optimization (BO) is an effective framework for globally optimizing functions whose evaluations are expensive. It is particularly effective for optimizing functions define…
Sampling-Based Global Optimal Control and Estimation via Semidefinite Programming
Antoine Groudiev, Fabian Schramm, Ãloïse Berthier +2
Global optimization has gained attraction over the past decades, thanks to the development of both theoretical foundations and efficient numerical routines. Among recent advances,…
Can Tabular Foundation Models Guide Exploration in Robot Policy Learning?
Buqing Ou, Frederike Dümbgen
Policy optimization in high-dimensional continuous control for robotics remains a challenging problem. Predominant methods are inherently local and often require extensive tuning a…
Global Sampling-Based Trajectory Optimization for Contact-Rich Manipulation via KernelSOS
Zhongqi Wei, Frederike Dümbgen
Contact-rich manipulation is challenging due to its high dimensionality, the requirement for long time horizons, and the presence of hybrid contact dynamics. Sampling-based methods…
A Data-driven Contact Estimation Method for Wheeled-Biped Robots
Ã. Bora Gökbakan, Frederike Dümbgen, Stéphane Caron
Contact estimation is a key ability for limbed robots, where making and breaking contacts has a direct impact on state estimation and balance control. Existing approaches typically…