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Actively Learning Gaussian Process Dynamics

arXiv 1911.09946 ↗First posted 22 Nov 2019cs.LG cs.RO stat.MLcited by 26

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Abstract (arXiv)

Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient way. We propose active learning strategies that leverage information-theoretical properties arising naturally during Gaussian process regression, while respecting constraints on the sampling process imposed by the system dynamics. Sample points are selected in regions with high uncertainty, leading to exploratory behavior and data-efficient training of the model. All results are finally verified in an extensive numerical benchmark.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Nov 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 2; 2023: 7; 2024: 5; 2025: 4; 2026: 3.

26 papers.

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  1. 2023Optimistic Active Exploration of Dynamical Systemscited by 32
  2. 2021An Experimental Design Perspective on Model-Based Reinforcement Learningcited by 16
  3. 2021Structure-preserving Gaussian Process Dynamicscited by 9
  4. 2025SOMBRL: Scalable and Optimistic Model-Based RLcited by 7
  5. 2021Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Modelscited by 6
  6. 2021Probabilistic Robust Linear Quadratic Regulators with Gaussian Processescited by 4
  7. 2024On the Consistency of Kernel Methods with Dependent Observationscited by 4
  8. 2024On Foundation Models for Dynamical Systems from Purely Synthetic Datacited by 4
  9. 2023Out of Distribution Detection via Domain-Informed Gaussian Process State Space Modelscited by 3
  10. 2024Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketchingcited by 3
  11. 2024First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handlingcited by 3
  12. 2021Active Learning in Gaussian Process State Space Modelcited by 2
14 more
  1. 2022The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Modelscited by 2
  2. 2023FLEX: an Adaptive Exploration Algorithm for Nonlinear Systemscited by 2
  3. 2023Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regressioncited by 2
  4. 2025Learning Soft Robotic Dynamics with Active Explorationcited by 2
  5. 2023Deep active learning for nonlinear system identificationcited by 1
  6. 2023Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spacescited by 1
  7. 2024Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processescited by 1
  8. 2026Active Sensing and Deferred-Decision Trajectory Optimization for Robust Target Identificationcited by 1
  9. 2022Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Datacited by 0
  10. 2023Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errorscited by 0
  11. 2025Comparison of Data Reduction Criteria for Online Gaussian Processescited by 0
  12. 2025Worst-Case Distance-Aware Error Bounds for Neural Networkscited by 0
  13. 2026Generalized Information Gathering Under Dynamics Uncertaintycited by 0
  14. 2026All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learningcited by 0