Dynamic Movement Primitives in Robotics: A Tutorial Survey

Abstract

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.

arXiv comment43 pages, 21 figures, 5 tables

References in corpus (14)

• Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions
• Overcoming Some Drawbacks of Dynamic Movement Primitives
• Towards Orientation Learning and Adaptation in Cartesian Space
• Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
• Geometry-aware Dynamic Movement Primitives
• Merging Position and Orientation Motion Primitives
• Learning Feedback Terms for Reactive Planning and Control
• Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
• Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
• Sparse Control for Dynamic Movement Primitives
• Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications
• Periodic DMP formulation for Quaternion Trajectories
• Human-robot collaborative object transfer using human motion prediction based on Cartesian pose Dynamic Movement Primitives
• Composing Diverse Policies for Temporally Extended Tasks

Cited by in corpus (18)

• Interactive Imitation Learning of Bimanual Movement Primitives
• A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing
• Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview
• Monte Carlo Tree Search with Spectral Expansion for Planning with Dynamical Systems
• Periodic DMP formulation for Quaternion Trajectories
• RAMPA: Robotic Augmented Reality for Machine Programming by DemonstrAtion
• Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower
• Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey
• LEGATO: Cross-Embodiment Imitation Using a Grasping Tool
• MMP++: Motion Manifold Primitives with Parametric Curve Models
• Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration
• Human-robot collaborative transport personalization via Dynamic Movement Primitives and velocity scaling
• A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks
• Comparison of Motion Encoding Frameworks on Human Manipulation Actions
• Phase-Amplitude Reduction-Based Imitation Learning
• ROPE: A Novel Method for Real-Time Phase Estimation of Complex Biological Rhythms
• KeyMPs: One-Shot Vision-Language Guided Motion Generation by Sequencing DMPs for Occlusion-Rich Tasks
• A Novel Dual Quaternion Based Dynamic Motion Primitives for Acrobatic Flight