Contraction Theory

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9 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Provable State Estimation with Recurrent Models
Nov 6, 2026Vincent Andrieu, Pauline Bernard, Lucas Brivadis +2Recurrent Neural Networks Robot State Estimation
LAGEPP, CNRS · LAGEPP, CNRS, UCBL 1 · CAS +4
Manifold-Stable Flow Matching
Sep 28, 2026Amirhossein Nazerian, Ali Pezeshki, Jianguo ZhaoConstrained Generative Modeling Flow Matching
Department of Mechanical Engineering Colorado State University Fort Collins, CO 80523, USA · Department of Electrical and Computer Engineering Colorado State University Fort Collins, CO 80523, USA
Impact-Time Guidance via Normal Contraction to a Time-to-Go Isochron
Sep 22, 2026Shivam Bajpai, Abhinav SinhaContraction Theory
Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati, OH, 45221, USA
Feedback Motion Planning for Stochastic Nonlinear Systems with Signal Temporal Logic Specifications
May 4, 2026Liqian Ma, Zishun Liu, Glen Chou +1Constrained Motion Planning Trajectory Optimization
Georgia Institute of Technology, Atlanta, GA 30332
A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
Apr 16, 2026Anand Gokhale, Anton V. Proskurnikov, Yu Kawano +1Dynamical Systems Contraction Theory
Center for Control, Dynamical Systems, and Computation, UC Santa Barbara, Santa Barbara, CA 93106 USA · Department of Electronics and Telecommunications, Politecnico di Torino, Italy, 10129 · Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan
Controller Design for Structured State-space Models via Contraction Theory
Apr 8, 2026Muhammad Zakwan, Vaibhav Gupta, Alireza Karimi +2Contraction Theory Feedback Control
Control & Automation Group, Inspire AG, 8005 Zürich, Switzerland · Automatic Control Laboratory (IfA), ETH Zürich, 8092 Zürich, Switzerland. · Laboratoire d’Automatique, EPFL, 1015 Lausanne, Switzerland.
Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems
Mar 7, 2026Rihan Aaron D'Silva, Hiroyasu TsukamotoContraction Theory Trajectory Optimization
Department of Aerospace Engineering, The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801
Riemannian Optimization in Modular Systems
Mar 4, 2026Christian Pehle, Jean-Jacques SlotineContraction Theory Riemannian Optimization
Cold Spring Harbor Laboratory Cold Spring Harbor, NY 11724 · Massachusetts Institute of Technology Cambridge, MA 02139
Environment-Aware Learning of Smooth GNSS Covariance Dynamics for Autonomous Racing
Feb 24, 2026Y. Deemo Chen, Arion Zimmermann, Thomas A. Berrueta +1Contraction Theory Covariance Estimation
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA.

Nov 6, 2026J/K move · Enter open · S save

Provable State Estimation with Recurrent Models

Vincent Andrieu, Pauline Bernard, Lucas Brivadis, Laurent Praly+1

LAGEPP, CNRS · LAGEPP, CNRS, UCBL 1 · CAS+4

Recurrent Neural Networks Robot State Estimation Contraction Theory

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Estimating the state of a dynamical system from observations is a crucial task in robotics and control. Despite their empirical success, recurrent models often lack formal guarantees on convergence and robustness. We bridge this gap by analyzing these architectures from the perspective of nonlinear observer theory. By framing RNNs as a time-discretization of continuous latent dynamical systems (Neural ODEs), we demonstrate that enforcing a contraction condition on the latent flow guarantees the existence of a smooth, injective map of the true physical state into the latent space. We prove that this architecture acts as a tunable observer: by increasing a single scalar gain that scales the latent vector field, the estimation error can be driven to an arbitrarily small neighborhood of zero at an exponentially fast rate. We further establish quantitative bounds on the MLP complexity required to decode this latent state, and extend our formal guarantees to the discrete-time residual RNN implementation. We validate the theory on a real mobile robotic task, where a provably contracting model outperforms a vanilla GRU with substantially more stable training. We also report that unconstrained GRUs converge to contracting dynamics on this task, suggesting that contraction is an emergent property of well-trained sequence models.