mlsanityData to 14 Oct 2026

Control-oriented meta-learning

arXiv 2204.06716 ↗First posted 14 Apr 2022cs.RO cs.LG cs.SYcited by 17

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Abstract (arXiv)

Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory tracking performance, provided that any uncertain dynamics terms are linearly parameterizable with known nonlinear features. However, it is often difficult to specify such features a priori, such as for aerodynamic disturbances on rotorcraft or interaction forces between a manipulator arm and various objects. In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal parametric model of these nonlinear features. Our key insight is that we can better prepare the controller for deployment with control-oriented meta-learning of features in closed-loop simulation, rather than regression-oriented meta-learning of features to fit input-output data. Specifically, we meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. With both fully-actuated and underactuated nonlinear planar rotorcraft subject to wind, we demonstrate that our adaptive controller outperforms other controllers trained with regression-oriented meta-learning when deployed in closed-loop for trajectory tracking control.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 38 of its 110 references that are in the map. First posted on arXiv 14 Apr 2022.

Builds on

38 of 110 references.

References · 38

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26 more
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  2. 2018Model-Based Reinforcement Learning via Meta-Policy Optimizationcited by 101
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 3; 2024: 5; 2025: 3; 2026: 2.

17 papers.

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  1. 2022Multi-Task Imitation Learning for Linear Dynamical Systemscited by 23
  2. 2024Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQRcited by 17
  3. 2024Feedback Favors the Generalization of Neural ODEscited by 8
  4. 2023Data-Driven Control with Inherent Lyapunov Stabilitycited by 6
  5. 2023Learning Control-Oriented Dynamical Structure from Datacited by 5
  6. 2024Decision Transformer as a Foundation Model for Partially Observable Continuous Controlcited by 5
  7. 2022Tractable Uncertainty-Aware Meta-Learningcited by 4
  8. 2022Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Predictioncited by 3
  9. 2024Combining Federated Learning and Control: A Surveycited by 3
  10. 2024First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handlingcited by 3
  11. 2025Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Drivingcited by 3
  12. 2022Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equationscited by 2
5 more
  1. 2025DiffCoord: Differentiable Coordination for Distributed Multi-Agent Trajectory Optimizationcited by 2
  2. 2025Personalized Collaborative Learning with Affinity-Based Variance Reductioncited by 1
  3. 2023META-SMGO-Delta: similarity as a prior in black-box optimizationcited by 0
  4. 2026APEX: Adaptive Policy Execution for Precise Manipulationcited by 0
  5. 2026Latent Lie-Poisson Neural Networks (LLPNNs): Discovering the motion of Lie-Poisson systems through observable data and latent dynamicscited by 0