Dynamic Movement Primitives for moving goals with temporal scaling adaptation

Koutras, Leonidas; Doulgeri, Zoe

doi:10.1109/icra40945.2020.9196765

Cited by 21 publications

(18 citation statements)

References 20 publications

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“…Inspired by the work [38], the temporal scaling can be adjusted based on the task and the velocity constraints. The target position and velocity update the shared temporal parameter in the position and orientation DMPs, and it may be described as [21],…”

Section: The Composite Dmp Formulationmentioning

confidence: 99%

“…s d is the temporal scaling coefficient in the demonstration. The temporal parameter update law has been proved to converge to the moving goals in [21].…”

Section: The Composite Dmp Formulationmentioning

confidence: 99%

“…The adaptability of robot skills often refers to spatial and temporal scaling, adjusting their behaviours based on the perception information. Such as tracking moving tasks, the robot needs to modify its trajectory based on the position and velocity of the moving target [21]. Besides, many specific tasks require the generalisation of robot skills, such as obstacle avoidance and performing tasks in dynamic environments and situations.…”

Section: Introductionmentioning

confidence: 99%

“…For dynamic tasks, such as tracking moving targets, it can be seen that many researchers proposed modified versions of DMPs to deal with moving goals. For example in [21], the authors modified the DMP by adjusting the temporal scaling parameter online to follow a moving goal, although it only focused on the position trajectory in Cartesian space. To improve the generalisation, single DMP could not produce complex behaviour.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: The Composite Dmp Formulationmentioning

confidence: 99%

“…s d is the temporal scaling coefficient in the demonstration. The temporal parameter update law has been proved to converge to the moving goals in [21].…”

Section: The Composite Dmp Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wang

Yang

2021

Neural Comput & Applic

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“…Here τ 0 and T 0 are the initial value of τ and initial value of the task execution time respectively. A similar approach of defining a first order dynamics on τ is provided in recent work [23]. Here, τ is adapted solely to maintain the demonstrated velocity levels in case of DMP execution for a task involving a moving goal.…”

Section: A Real-time Control Of the Dmp Execution Timementioning

confidence: 99%

Real-time temporal adaptation of dynamic movement primitives for moving targets

Anand

Østvik

Grøtli

et al. 2021

2021 20th International Conference on Advanced Robotics (ICAR)

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This work is aimed at extending the standard dynamic movement primitives (DMP) framework to adapt to real-time changes in the task execution time while preserving its style characteristics. We propose an alternative polynomial canonical system and an adaptive law allowing a higher degree of control over the execution time. The extended framework has a potential application in robotic manipulation tasks that involve moving objects demanding real-time control over the task execution time. The existing methods require a computationally expensive forward simulation of DMP at every time step which makes it undesirable for integration in realtime control systems. To address this deficiency, the behaviour of the canonical system has been adapted according to the changes in the desired execution time of the task performed. An alternative polynomial canonical system is proposed to provide increased real-time control on the temporal scaling of DMP system compared to the standard exponential canonical system. The developed method was evaluated on scenarios of tracking a moving target where the desired tracking time is varied in real-time. The results presented show that the extended version of DMP provide better control over the temporal scaling during the execution of the task. We have evaluated our approach on a UR5 robotic manipulator for tracking a moving object.

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A model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter

Wang

et al. 2022

Asian Journal of Control

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A dynamic motion primitive (DMP) is a robust framework that generates obstacle avoidance trajectories by introducing perturbative terms. The perturbative term is usually constructed with an artificial potential field (APF) method.Dynamic obstacle avoidance is rarely considered with this approach; furthermore, even when dynamic obstacles are considered, only the velocity and position information of the current state are incorporated into the obstacle avoidance framework. However, if the position of an obstacle changes suddenly, a robot may be placed in a dangerous position close to the obstacle, resulting in large obstacle avoidance accelerations, sharp trajectories, or even obstacle avoidance failure. Therefore, we present a model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter. This method has three main components: Dynamic motion primitives are used to generate the desired trajectory and introduce perturbations to achieve obstacle avoidance; the Kalman filter method is adopted to estimate the future positions of the obstacles; and model predictive control is employed to optimize the repulsive force generated by the APF while minimizing the defined cost function, thus guaranteeing the safety and flexibility of the method. We validate the presented method with 2D and 3D obstacle avoidance simulations. The method is also verified with a real robot: the-Kinova MOVO. The simulation and experimental results show that the proposed method not only avoids dynamic obstacles but also tracks the desired trajectory more smoothly and precisely. KEYWORDS artificial potential field (APF), dynamic motion primitive (DMP), dynamic obstacle avoidance, kalman filter, model predictive control (MPC)

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Dynamic Movement Primitives for moving goals with temporal scaling adaptation

Cited by 21 publications

References 20 publications

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

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

Real-time temporal adaptation of dynamic movement primitives for moving targets

A model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter

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