Merging Position and orientation Motion Primitives

Saveriano, Matteo; Franzel, Felix; Lee, Dongheui

doi:10.1109/icra.2019.8793786

Cited by 48 publications

(42 citation statements)

References 38 publications

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“…DMPs have been extended in several ways. Among the others: [15] tests different approaches to chain multiple DMPs representing position and orientation trajectories, while [16] introduces task-dependent parameters to customize the execution. The timedependency is the main drawback of DMPs, since it limits the generalization capabilities pf the DMP outside the demonstration area [17].…”

Section: Motion Planning With Dynamical Systemsmentioning

confidence: 99%

Combining decision making and dynamical systems for monitoring and executing manipulation tasks

Saveriano

Piater

2020

Elektrotech. Inftech.

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In this paper, we propose a unified framework for online task scheduling, monitoring, and execution that integrates reconfigurable behavior trees, a decision-making framework with integrated low-level control functionalities, and reactive motion generation with stable dynamical systems. In this way, we realize a flexible and reactive system capable of coping with unexpected variations in the executive context without penalizing modularity, expressiveness, and readability of humans. The framework is evaluated in a simulated sorting task showing promising results in terms of flexibility regarding task scheduling and robustness to external disturbances.

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Section: Motion Planning With Dynamical Systemsmentioning

confidence: 99%

Combining decision making and dynamical systems for monitoring and executing manipulation tasks

Saveriano

Piater

2020

Elektrotech. Inftech.

Self Cite

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“…To improve the generalisation, single DMP could not produce complex behaviour. Merging the different DMP is very important to deal with this challenge [24]. It also pointed out that building a motion skill library for robots to produce complex behaviour is a useful tool.…”

Section: Introductionmentioning

confidence: 99%

“…Complex trajectories involving several actions can be reproduced by sequencing multiple motion primitives. Each motion primitive is represented as DMP; various approaches were investigated to connect the motion primitives seamlessly [24]. However, most of the current work focused on the position motion primitives in Cartesian or joint space, and there is a lack of research on the orientation primitive trajectories in Cartesian.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Yang

2021

Neural Comput & Applic

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In this paper, composite dynamic movement primitives (DMPs) based on radial basis function neural networks (RBFNNs) are investigated for robots’ skill learning from human demonstrations. The composite DMPs could encode the position and orientation manipulation skills simultaneously for human-to-robot skills transfer. As the robot manipulator is expected to perform tasks in unstructured and uncertain environments, it requires the manipulator to own the adaptive ability to adjust its behaviours to new situations and environments. Since the DMPs can adapt to uncertainties and perturbation, and spatial and temporal scaling, it has been successfully employed for various tasks, such as trajectory planning and obstacle avoidance. However, the existing skill model mainly focuses on position or orientation modelling separately; it is a common constraint in terms of position and orientation simultaneously in practice. Besides, the generalisation of the skill learning model based on DMPs is still hard to deal with dynamic tasks, e.g., reaching a moving target and obstacle avoidance. In this paper, we proposed a composite DMPs-based framework representing position and orientation simultaneously for robot skill acquisition and the neural networks technique is used to train the skill model. The effectiveness of the proposed approach is validated by simulation and experiments.

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“…Dynamical systems (DS) are a promising approach to represent demonstrated skills and plan robotic motions in real-time. DS have been successfully used in a variety of robotic applications including point-to-point motion planning [3,6,14,17], reactive motion replanning [1,2,5,7], and learning impedance behaviors from demonstrations [18,23].…”

Section: Introductionmentioning

confidence: 99%

Incremental Motion Reshaping of Autonomous Dynamical Systems

Saveriano

Lee

2020

Springer Proceedings in Advanced Robotics

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This paper presents an approach to incrementally learn a reshaping term that modifies the trajectories of an autonomous dynamical system without affecting its stability properties. The reshaping term is considered as an additive control input and it is incrementally learned from human demonstrations using Gaussian process regression. We propose a novel parametrization of this control input that preserves the time-independence and the stability of the reshaped system, as analytically proved in the performed Lyapunov stability analysis. The effectiveness of the proposed approach is demonstrated with simulations and experiments on a real robot.

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Merging Position and orientation Motion Primitives

Cited by 48 publications

References 38 publications

Combining decision making and dynamical systems for monitoring and executing manipulation tasks

Combining decision making and dynamical systems for monitoring and executing manipulation tasks

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

Incremental Motion Reshaping of Autonomous Dynamical Systems

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