mlsanityData to 7 Oct 2026

Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview

arXiv 2403.19916 ↗First posted 29 Mar 2024cs.ROcited by 3

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL lies in extending generalization from historical demonstrations, enabling the acquisition of new skills without re-teaching. Dynamical system-based IL (DSIL) emerges as a significant subset of IL methodologies, offering the ability to learn trajectories via movement primitives and policy learning based on experiential abstraction. This paper emphasizes the fusion of theoretical paradigms, integrating control theory principles inherent in dynamical systems into IL. This integration notably enhances robustness, adaptability, and convergence in the face of novel scenarios. This survey aims to present a comprehensive overview of DSIL methods, spanning from classical approaches to recent advanced approaches. We categorize DSIL into autonomous dynamical systems and non-autonomous dynamical systems, surveying traditional IL methods with low-dimensional input and advanced deep IL methods with high-dimensional input. Additionally, we present and analyze three main stability methods for IL: Lyapunov stability, contraction theory, and diffeomorphism mapping. Our exploration also extends to popular policy improvement methods for DSIL, encompassing reinforcement learning, deep reinforcement learning, and evolutionary strategies.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 56 of its 215 references that are in the map. First posted on arXiv 29 Mar 2024.

Builds on

56 of 215 references.

Closest ancestors · 8

Same direction, newest first

  1. 2023Learning Lyapunov-Stable Polynomial Dynamical Systems through Imitation31 Oct
  2. 2023End-to-End Stable Imitation Learning via Autonomous Neural Dynamic Policies22 May
  3. 2021Adversarial Imitation Learning with Trajectorial Augmentation and Correction25 Mar
  4. 2021Off-Policy Imitation Learning from Observations25 Feb
  5. 2019Task-Relevant Adversarial Imitation Learning2 Oct
  6. 2018HG-DAgger: Interactive Imitation Learning with Human Experts5 Oct
  7. 2018EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning22 Jul
  8. 2018Learning Contracting Vector Fields For Stable Imitation Learning13 Apr

Other references · 48

  1. 2018Neural Ordinary Differential Equationscited by 3,733
  2. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  3. 2017Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translationcited by 167
  4. 2023Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisitioncited by 156
  5. 2020Learning Stable Deep Dynamics Modelscited by 110
  6. 2022VideoDex: Learning Dexterity from Internet Videoscited by 101
  7. 2019Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Taskscited by 56
  8. 2020Neural Dynamic Policies for End-to-End Sensorimotor Learningcited by 56
  9. 2021Towards More Generalizable One-shot Visual Imitation Learningcited by 48
  10. 2022Retrospectives on the Embodied AI Workshopcited by 46
  11. 2021Dynamic Movement Primitives in Robotics: A Tutorial Surveycited by 45
  12. 2021Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overviewcited by 42
36 more
  1. 2020ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flowscited by 33
  2. 2019Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularizationcited by 32
  3. 2020Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systemscited by 32
  4. 2022ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitivescited by 25
  5. 2022Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objectscited by 24
  6. 2021CRIL: Continual Robot Imitation Learning via Generative and Prediction Modelcited by 23
  7. 2020Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulationcited by 22
  8. 2021Hierarchical Neural Dynamic Policiescited by 17
  9. 2022Learning Riemannian Stable Dynamical Systems via Diffeomorphismscited by 17
  10. 2020Variable impedance control and learning -- A reviewcited by 13
  11. 2021Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approachcited by 13
  12. 2021Diffeomorphically Learning Stable Koopman Operatorscited by 13
  13. 2020Merging Position and Orientation Motion Primitivescited by 12
  14. 2021Learning Stable Vector Fields on Lie Groupscited by 12
  15. 2022Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrationscited by 10
  16. 2022Learning Stable Robotic Skills on Riemannian Manifoldscited by 10
  17. 2020An Energy-based Approach to Ensure the Stability of Learned Dynamical Systemscited by 7
  18. 2022Human-Robot Shared Control for Surgical Robot Based on Context-Aware Sim-to-Real Adaptationcited by 7
  19. 2023Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Modelcited by 6
  20. 2019Teleoperator Imitation with Continuous-time Safetycited by 5
  21. 2020Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functionscited by 5
  22. 2022Learning Deep Robotic Skills on Riemannian manifoldscited by 5
  23. 2020Geometry-aware Dynamic Movement Primitivescited by 4
  24. 2022Learning Skills from Demonstrations: A Trend from Motion Primitives to Experience Abstractioncited by 4
  25. 2022Learning Stable Dynamical Systems for Visual Servoingcited by 3
  26. 2022Generating Stable and Collision-Free Policies through Lyapunov Function Learningcited by 3
  27. 2021Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learningcited by 2
  28. 2021Periodic DMP formulation for Quaternion Trajectoriescited by 2
  29. 2022Impedance Adaptation by Reinforcement Learning with Contact Dynamic Movement Primitivescited by 2
  30. 2023Deep Segmented DMP Networks for Learning Discontinuous Motionscited by 2
  31. 2019Learning Generalisable Coupling Terms for Obstacle Avoidance via Low-dimensional Geometric Descriptorscited by 1
  32. 2021Human-robot collaborative object transfer using human motion prediction based on Cartesian pose Dynamic Movement Primitivescited by 1
  33. 2022Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmapscited by 1
  34. 2023A Passivity-based Approach for Variable Stiffness Control with Dynamical Systemscited by 1
  35. 2023Orientation Control with Variable Stiffness Dynamical Systemscited by 1
  36. 2024Safe Execution of Learned Orientation Skills with Conic Control Barrier Functionscited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025Information-Theoretic Graph Fusion with Vision-Language-Action Model for Policy Reasoning and Dual Robotic Controlcited by 2
  2. 2024A context-adaptive policy framework for robust and reactive robotic manipulation via uncertainty-aware imitation learningcited by 0
  3. 2025TamedPUMA: safe and stable imitation learning with geometric fabricscited by 0

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  3. 2022Interactive Imitation Learning in Robotics: A Survey
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  5. 2018An Algorithmic Perspective on Imitation Learning
  6. 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances
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