Composite dynamic movement primitives based on neural networks for human–robot skill transfer

Si, Weiyong; Wang, Ning; Yang, Chenguang

doi:10.1007/s00521-021-05747-8

Cited by 14 publications

(3 citation statements)

References 39 publications

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“…LWR is widely used when the forcing term is a combination of RBFs as in (4). However, in the literature one can use RBF-NN as in Si et al (2021) or if multiple demonstrations are given, one can exploit GMM/GMR as in Li et al (2021b) and Pervez et al (2017a) or GPR as in Fanger et al (2016) to represent the forcing term and use expectation–maximization to fit the (hyper-)parameters. Deep NNs, typically trained via back-propagation, seem an appealing possibility to map input images into forcing terms (Pervez et al, 2017b), mimicking the human perception-action loop.…”

Section: Discussionmentioning

confidence: 99%

Dynamic movement primitives in robotics: A tutorial survey

Saveriano,

Abu-Dakka,

Kramberger

et al. 2023

The International Journal of Robotics Research

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Biological systems, including human beings, have the innate ability to perform complex tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to comprehend and formally define this innate characteristic. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally leads 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 in a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMP formulations in rigorous mathematical terms and discuss the 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.

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Section: Discussionmentioning

confidence: 99%

Dynamic movement primitives in robotics: A tutorial survey

Saveriano,

Abu-Dakka,

Kramberger

et al. 2023

The International Journal of Robotics Research

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show abstract

“…DMPs can be used to model both periodic and discrete motion skills. Currently, most research on DMPs mainly focuses on the position and orientation DMPs and their modifications (Lu et al, 2021a;Si et al, 2021a), which can be used to represent arbitrary movements for robots in Cartesian or joint space by adding a nonlinear term to adjust the shape of trajectory. Also, the DMPs can be used to model the force profiles (Zhang et al, 2021) and stiffness profiles (Zeng et al, 2018).…”

Section: Dynamic Movement Primitivesmentioning

confidence: 99%

A Framework for Composite Layup Skill Learning and Generalizing Through Teleoperation

Wang

et al. 2022

Front. Neurorobot.

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In this article, an impedance control-based framework for human-robot composite layup skill transfer was developed, and the human-in-the-loop mechanism was investigated to achieve human-robot skill transfer. Although there are some works on human-robot skill transfer, it is still difficult to transfer the manipulation skill to robots through teleoperation efficiently and intuitively. In this article, we developed an impedance-based control architecture of telemanipulation in task space for the human-robot skill transfer through teleoperation. This framework not only achieves human-robot skill transfer but also provides a solution to human-robot collaboration through teleoperation. The variable impedance control system enables the compliant interaction between the robot and the environment, smooth transition between different stages. Dynamic movement primitives based learning from demonstration (LfD) is employed to model the human manipulation skills, and the learned skill can be generalized to different tasks and environments, such as the different shapes of components and different orientations of components. The performance of the proposed approach is evaluated on a 7 DoF Franka Panda through the robot-assisted composite layup on different shapes and orientations of the components.

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“…Motion generation uses classic DMPs method with velocity limits inspired by ref. [23] to learn the corrected path. Single execution part designs a remote control system to ensure the end-effector of manipulator following the learned trajectory, where radial basis function neural network (RBFNN) is employed to approximate the unknown robot dynamics.…”

Section: Introductionmentioning

confidence: 99%

A constrained framework based on IBLF for robot learning with human supervision

Shi

Yang

et al. 2023

Robotica

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Dynamical movement primitives (DMPs) method is a useful tool for efficient robotic skills learning from human demonstrations. However, the DMPs method should know the specified constraints of tasks in advance. One flexible solution is to introduce the human superior experience as part of input. In this paper, we propose a framework for robot learning based on demonstration and supervision. Superior experience supplied by teleoperation is introduced to deal with unknown environment constrains and correct the demonstration for next execution. DMPs model with integral barrier Lyapunov function is used to deal with the constrains in robot learning. Additionally, a radial basis function neural network based controller is developed for teleoperation and the robot to track the generated motions. Then, we prove convergence of the generated path and controller. Finally, we deploy the novel framework with two touch robots to certify its effectiveness.

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Composite dynamic movement primitives based on neural networks for human–robot skill transfer

Cited by 14 publications

References 39 publications

Dynamic movement primitives in robotics: A tutorial survey

Dynamic movement primitives in robotics: A tutorial survey

A Framework for Composite Layup Skill Learning and Generalizing Through Teleoperation

A constrained framework based on IBLF for robot learning with human supervision

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