L1-GP: L1 Adaptive Control with Bayesian Learning
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Abstract (arXiv)
We present L1-GP, an architecture based on L1 adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learning. On one hand, the L1 adaptive control provides stability and transient performance guarantees, which allows for GPR to efficiently and safely learn the uncertain dynamics. On the other hand, the learned dynamics can be conveniently incorporated into the L1 control architecture without sacrificing robustness and tracking performance. Subsequently, the learned dynamics can lead to less conservative designs for performance/robustness tradeoff. We illustrate the efficacy of the proposed architecture via numerical simulations.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2020.
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Citing papers per year: 2021: 5; 2022: 5; 2023: 1.
11 papers.
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
- 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 169
- 2022Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Controlcited by 13
- 2021Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Featurescited by 6
- 2022Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processescited by 5
- 2022Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Modelscited by 3
- 2022Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processescited by 2
- 2021Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Informationcited by 1
- 2023Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A reviewcited by 1
- 2021Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environmentscited by 0
- 2021Robustifying Reinforcement Learning Policies with L1 Adaptive Controlcited by 0
- 2023Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review16 Jul 2023
- 2022Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control23 Oct 2022
- 2022Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes24 Jun 2022
- 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applications20 May 2022
- 2022Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models11 May 2022
- 2022Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes23 Feb 2022
- 2021Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features21 Sep 2021
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning13 Aug 2021
- 2021Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information19 Jun 2021
- 2021Robustifying Reinforcement Learning Policies with L1 Adaptive Control4 Jun 2021
- 2021Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments13 Mar 2021