37 citations · 64 across the 15 of their papers we have counts for
17 papers
A Predictive Law for On-Policy Self-Distillation From World Feedback
Tommy He, Jerome Sieber, Matteo Saponati
Moving beyond simple scalar rewards toward richer world feedback is a natural path to more scalable RL post-training. On-policy self-distillation (OPSD) is a promising recent appro…
Design Principles for Sequence Models via Coefficient Dynamics
Jerome Sieber, Antonio Orvieto, Melanie N. Zeilinger +1
Deep sequence models, ranging from Transformers and State Space Models (SSMs) to more recent approaches such as gated linear RNNs, fundamentally compute outputs as linear combinati…
Eigenvalues as a Metric for Memory Dynamics in Sequence Models
Rahel Rickenbach, Jelena Trisovic, Alexandre Didier +2
While softmax attention drives state-of-the-art performance in sequence modeling, its quadratic complexity motivates linear alternatives such as state space models (SSMs). Structur…
Stochastic MPC with Online-optimized Policies and Closed-loop Guarantees
Marcell Bartos, Alexandre Didier, Jerome Sieber +2
This paper proposes a stochastic model predictive control method for linear systems affected by additive Gaussian disturbances that optimizes over disturbance feedback matrices onl…
Lambda-Skip Connections: the architectural component that prevents Rank Collapse
Federico Arangath Joseph, Jerome Sieber, Melanie N. Zeilinger +1
Rank collapse, a phenomenon where embedding vectors in sequence models rapidly converge to a uniform token or equilibrium state, has recently gained attention in the deep learning…
Computationally Efficient System Level Tube-MPC for Uncertain Systems
Jerome Sieber, Alexandre Didier, Melanie N. Zeilinger
Tube-based model predictive control (MPC) is one of the principal robust control techniques for constrained linear systems affected by additive disturbances. While tube-based metho…
Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks
Jerome Sieber, Carmen Amo Alonso, Alexandre Didier +2
Softmax attention is the principle backbone of foundation models for various artificial intelligence applications, yet its quadratic complexity in sequence length can limit its inf…
State Space Models as Foundation Models: A Control Theoretic Overview
Carmen Amo Alonso, Jerome Sieber, Melanie N. Zeilinger
In recent years, there has been a growing interest in integrating linear state-space models (SSM) in deep neural network architectures of foundation models. This is exemplified by…
Robust Optimal Control for Nonlinear Systems with Parametric Uncertainties via System Level Synthesis
Antoine P. Leeman, Jerome Sieber, Samir Bennani +1
This paper addresses the problem of optimally controlling nonlinear systems with norm-bounded disturbances and parametric uncertainties while robustly satisfying constraints. The p…
Approximate Predictive Control Barrier Functions using Neural Networks: A Computationally Cheap and Permissive Safety Filter
Alexandre Didier, Robin C. Jacobs, Jerome Sieber +2
A predictive control barrier function (PCBF) based safety filter is a modular framework to verify safety of a control input by predicting a future trajectory. The approach relies o…
Asynchronous Computation of Tube-based Model Predictive Control
Jerome Sieber, Andrea Zanelli, Antoine P. Leeman +2
Tube-based model predictive control (MPC) methods bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. While techniques that…
Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and control
Andrea Carron, Sabrina Bodmer, Lukas Vogel +6
From both an educational and research point of view, experiments on hardware are a key aspect of robotics and control. In the last decade, many open-source hardware and software fr…
A System Level Approach to Regret Optimal Control
Alexandre Didier, Jerome Sieber, Melanie N. Zeilinger
We present an optimisation-based method for synthesising a dynamic regret optimal controller for linear systems with potentially adversarial disturbances and known or adversarial i…
System Level Disturbance Reachable Sets and their Application to Tube-based MPC
Jerome Sieber, Andrea Zanelli, Samir Bennani +1
Tube-based model predictive control (MPC) methods leverage tubes to bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. This…
A System Level Approach to Tube-based Model Predictive Control
Jerome Sieber, Samir Bennani, Melanie N. Zeilinger
Robust tube-based model predictive control (MPC) methods address constraint satisfaction by leveraging an a priori determined tube controller in the prediction to tighten the const…
Design, Optimal Guidance and Control of a Low-cost Re-usable Electric Model Rocket
Lukas Spannagl, Elias Hampp, Andrea Carron +5
In the last decade, autonomous vertical take-off and landing (VTOL) vehicles have become increasingly important as they lower mission costs thanks to their re-usability. However, t…
Distributed Safe Learning using an Invariance-based Safety Framework
Andrea Carron, Jerome Sieber, Melanie N. Zeilinger
In large-scale networks of uncertain dynamical systems, where communication is limited and there is a strong interaction among subsystems, learning local models and control policie…