6 papers
Co-VLA: Consensus-based Federated Training for Vision-Language-Action Models
Haolong Li, Guner Dilsad Er, Michael Muehlebach +1
Vision-language-action models (VLAs) have emerged as a promising paradigm for general-purpose robot learning, with performance improving as models and datasets scale. Scaling robot…
Teaching Machine Learning Fundamentals with LEGO Robotics
Viacheslav Sydora, Guner Dilsad Er, Michael Muehlebach
This paper presents the web-based platform Machine Learning with Bricks and an accompanying two-day course designed to teach machine learning concepts to students aged 12 to 17 thr…
A Systems-Theoretic View on the Convergence of Algorithms under Disturbances
Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach
Algorithms increasingly operate within complex physical, social, and engineering systems where they are exposed to disturbances, noise, and interconnections with other dynamical sy…
Controlling Participation in Federated Learning with Feedback
Michael Cummins, Guner Dilsad Er, Michael Muehlebach
We address the problem of client participation in federated learning, where traditional methods typically rely on a random selection of a small subset of clients for each training…
Distributed Event-Based Learning via ADMM
Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach
We consider a distributed learning problem, where agents minimize a global objective function by exchanging information over a network. Our approach has two distinct features: (i)…
Stochastic stability analysis of legged locomotion using unscented transformation
Guner Dilsad ER, Mustafa Mert Ankarali
In this manuscript, we present a novel method for estimating the stochastic stability characteristics of metastable legged systems using the unscented transformation. Prior methods…