Safe Feedback Motion Planning: A Contraction Theory and L1-Adaptive Control Based Approach
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Abstract (arXiv)
Autonomous robots that are capable of operating safely in the presence of imperfect model knowledge or external disturbances are vital in safety-critical applications. In this paper, we present a planner-agnostic framework to design and certify safe tubes around desired trajectories that the robot is always guaranteed to remain inside of. By leveraging recent results in contraction analysis and L1-adaptive control we synthesize an architecture that induces safe tubes for nonlinear systems with state and time-varying uncertainties. We demonstrate with a few illustrative examples how contraction theory-based L1-adaptive control can be used in conjunction with traditional motion planning algorithms to obtain provably safe trajectories.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Apr 2020.
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Citing papers per year: 2021: 5; 2022: 2; 2023: 0; 2024: 2; 2025: 1; 2026: 1.
11 papers.
- 2021Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overviewcited by 42
- 2021Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approachcited by 13
- 2021Model Error Propagation via Learned Contraction Metrics for Safe Feedback Motion Planning of Unknown Systemscited by 13
- 2022Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systemscited by 11
- 2022Neural Moving Horizon Estimation for Robust Flight Controlcited by 5
- 2024Federated reinforcement learning for robot motion planning with zero-shot generalizationcited by 3
- 2024Robust Model Based Reinforcement Learning Using L1 Adaptive Controlcited by 1
- 2025Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomycited by 1
- 2021Robustifying Reinforcement Learning Policies with L1 Adaptive Controlcited by 0
- 2021Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimationcited by 0
- 2026Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systemscited by 0
- 2026Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems7 Mar 2026
- 2025Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy19 Dec 2025
- 2024Robust Model Based Reinforcement Learning Using L1 Adaptive Control21 Mar 2024
- 2024Federated reinforcement learning for robot motion planning with zero-shot generalization20 Mar 2024
- 2022Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems13 Dec 2022
- 2022Neural Moving Horizon Estimation for Robust Flight Control21 Jun 2022
- 2021Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation15 Dec 2021
- 2021Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview1 Oct 2021
- 2021Robustifying Reinforcement Learning Policies with L1 Adaptive Control4 Jun 2021
- 2021Model Error Propagation via Learned Contraction Metrics for Safe Feedback Motion Planning of Unknown Systems18 Apr 2021
- 2021Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach25 Feb 2021