mlsanityData to 14 Oct 2026

Dynamic Movement Primitives in Robotics: A Tutorial Survey

arXiv 2102.03861 ↗First posted 7 Feb 2021cs.ROcited by 47

Not on the map. Why?

Abstract (arXiv)

Biological systems, including human beings, have the innate ability to perform complex tasks in versatile and agile manner. Researchers in sensorimotor control have tried to understand and formally define this innate property. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally lead to the formulation of the motor primitives theory. In this respect, Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems, and are well suited to generate motor commands for artificial systems like robots. In the last decades, DMPs have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control,physical interaction, and human-robot co-working, resulting a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMPs formulations in rigorous mathematical terms,and discuss advantages and limitations of each approach as well as practical implementation details. In the tutorial vein, we also search for existing implementations of presented approaches and release several others. On the other side, we provide a systematic and comprehensive review of existing literature and categorize state of the art work on DMP. The paper concludes with a discussion on the limitations of DMPs and an outline of possible research directions.

Key facts. Cited by 47 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Feb 2021.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 8; 2023: 8; 2024: 13; 2025: 10; 2026: 7.

47 papers.

Sort:

By area:

  1. 2022ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitivescited by 25
  2. 2022Interactive Imitation Learning of Bimanual Movement Primitivescited by 18
  3. 2025SAIL: Faster-than-Demonstration Execution of Imitation Learning Policiescited by 18
  4. 2023SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representationcited by 17
  5. 2025A Survey on Imitation Learning for Contact-Rich Tasks in Roboticscited by 14
  6. 2022Continual Learning from Demonstration of Robotics Skillscited by 12
  7. 2022Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrationscited by 10
  8. 2022ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interactioncited by 7
  9. 2023Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Modelcited by 7
  10. 2023MMP++: Motion Manifold Primitives with Parametric Curve Modelscited by 5
  11. 2024Fast Ergodic Search with Kernel Functionscited by 5
  12. 2022Multiscale Sensor Fusion and Continuous Control with Neural CDEscited by 4
35 more
  1. 2024Autonomous Catheterization with Open-source Simulator and Expert Trajectorycited by 4
  2. 2024Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learningcited by 4
  3. 2025Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Surveycited by 4
  4. 2023Style-transfer based Speech and Audio-visual Scene Understanding for Robot Action Sequence Acquisition from Videoscited by 3
  5. 2023Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planningcited by 3
  6. 2024Conditional Neural Expert Processes for Learning Movement Primitives from Demonstrationcited by 3
  7. 2024Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overviewcited by 3
  8. 2021Periodic DMP formulation for Quaternion Trajectoriescited by 2
  9. 2022Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blowercited by 2
  10. 2022Constrained Dynamic Movement Primitives for Safe Learning of Motor Skillscited by 2
  11. 2023Guided Learning from Demonstration for Robust Transferabilitycited by 2
  12. 2024Motion Manifold Flow Primitives for Task-Conditioned Trajectory Generation under Complex Task-Motion Dependenciescited by 2
  13. 2024Variable-Frequency Imitation Learning for Variable-Speed Motioncited by 2
  14. 2025A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Roboticscited by 2
  15. 2025Multifunctional physical reservoir computing in soft tensegrity robotscited by 2
  16. 2024Conditional Variational Auto Encoder Based Dynamic Motion for Multi-task Imitation Learningcited by 1
  17. 2024Enabling robots to follow abstract instructions and complete complex dynamic taskscited by 1
  18. 2024Mastering Contact-rich Tasks by Combining Soft and Rigid Robotics with Imitation Learningcited by 1
  19. 2025FRMD: Fast Robot Motion Diffusion with Consistency-Distilled Movement Primitives for Smooth Action Generationcited by 1
  20. 2025Learning Task-Agnostic Motifs to Capture the Continuous Nature of Animal Behaviorcited by 1
  21. 2025Small transformer architectures for task switchingcited by 1
  22. 2026Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learningcited by 1
  23. 2026POIL: Point-based One-Shot Imitation Learning with Stable Dynamical Systemscited by 1
  24. 2023Robotic Fabric Flattening with Wrinkle Direction Detectioncited by 0
  25. 2023Deep Learning for Koopman-based Dynamic Movement Primitivescited by 0
  26. 2024Phase-Amplitude Reduction-Based Imitation Learningcited by 0
  27. 2024One-Shot Robust Imitation Learning for Long-Horizon Visuomotor Tasks from Unsegmented Demonstrationscited by 0
  28. 2024RAMPA: Robotic Augmented Reality for Machine Programming by DemonstrAtioncited by 0
  29. 2025A Robust Task-Level Control Architecture for Learned Dynamical Systemscited by 0
  30. 2025Safe and Optimal Variable Impedance Control via Certified Reinforcement Learningcited by 0
  31. 2026Instruct2Act: From Human Instruction to Actions Sequencing and Execution via Robot Action Network for Robotic Manipulationcited by 0
  32. 2026FODMP: Fast One-Step Diffusion of Movement Primitives Generation for Time-Dependent Robot Actionscited by 0
  33. 2026VE2VF: Vision-Enabled to Vision-Free Distillation via Real-world Reinforcement Learning for Robust Contact-Rich Manipulationcited by 0
  34. 2026CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learningcited by 0
  35. 2026MaCoPlanner: LLM-Assisted Manual-Compiled Task Planning with Proactive Safety Verification for Robotic Industrial Panel Operationcited by 0