{"total":21,"rows":[{"id":"2110.00675","title":"Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview","created":"2021-10-01","cited_by_count":45,"area":null},{"id":"2206.06553","title":"Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory","created":"2022-06-14","cited_by_count":19,"area":null},{"id":"2201.01918","title":"SABLAS: Learning Safe Control for Black-box Dynamical Systems","created":"2022-01-06","cited_by_count":17,"area":null},{"id":"2401.09352","title":"Neural Contractive Dynamical Systems","created":"2024-01-17","cited_by_count":14,"area":null},{"id":"2102.12668","title":"Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach","created":"2021-02-25","cited_by_count":13,"area":null},{"id":"2104.08695","title":"Model Error Propagation via Learned Contraction Metrics for Safe Feedback Motion Planning of Unknown Systems","created":"2021-04-18","cited_by_count":13,"area":null},{"id":"2201.12243","title":"Joint Differentiable Optimization and Verification for Certified Reinforcement Learning","created":"2022-01-28","cited_by_count":11,"area":null},{"id":"2212.06874","title":"Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems","created":"2022-12-13","cited_by_count":11,"area":null},{"id":"2110.00693","title":"A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability","created":"2021-10-02","cited_by_count":6,"area":null},{"id":"2506.01356","title":"Two-Stage Learning of Stabilizing Neural Controllers via Zubov Sampling and Iterative Domain Expansion","created":"2025-06-02","cited_by_count":6,"area":null},{"id":"2504.17102","title":"Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems","created":"2025-04-23","cited_by_count":5,"area":null},{"id":"2305.03608","title":"On the Optimality, Stability, and Feasibility of Control Barrier Functions: An Adaptive Learning-Based Approach","created":"2023-05-05","cited_by_count":4,"area":null},{"id":"2411.18235","title":"Certified Training with Branch-and-Bound for Lyapunov-stable Neural Control","created":"2024-11-27","cited_by_count":3,"area":null},{"id":"2203.15474","title":"Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety","created":"2022-03-29","cited_by_count":2,"area":null},{"id":"2103.02987","title":"Learning-based Adaptive Control using Contraction Theory","created":"2021-03-04","cited_by_count":1,"area":null},{"id":"2411.11405","title":"Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions","created":"2024-11-18","cited_by_count":1,"area":null},{"id":"2504.18860","title":"Diffeomorphic Obstacle Avoidance for Contractive Dynamical Systems via Implicit Representations","created":"2025-04-26","cited_by_count":1,"area":null},{"id":"2605.26577","title":"Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial","created":"2026-05-26","cited_by_count":1,"area":null},{"id":"2112.08222","title":"Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation","created":"2021-12-15","cited_by_count":0,"area":null},{"id":"2510.10214","title":"Distributionally Robust Control with End-to-End Statistically Guaranteed Metric Learning","created":"2025-10-11","cited_by_count":0,"area":null},{"id":"2603.07092","title":"Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems","created":"2026-03-07","cited_by_count":0,"area":null}],"next_cursor":null}