Convergence of Dynamical Movement Primitives with Temporal Coupling

Karlsson, Martin; Robertsson, Anders; Johansson, Rolf

doi:10.23919/ecc.2018.8550135

Cited by 13 publications

(17 citation statements)

References 14 publications

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“…The orientation in DMP is often represented by rotation matrix or quaternions. For example in [33], the unit quaternions are used to model the orientation, and the unit quaternion set minus one single point also has been proved to be contractible [34]. This property of the unit quaternion set could guarantee the convergence of orientation DMPs.…”

Section: Position and Orientation Dmp In Cartesian Spacementioning

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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Section: Position and Orientation Dmp In Cartesian Spacementioning

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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“…The proposed formulation alleviates the problems of the original DMP as it does not scale each trajectory coordinate separately, but performs a three-dimensional scaling, involving both a rotation and a magnitude scaling. To study the spatial scaling of the proposed DMP we will again derive the trajectory tracking dynamics, by substituting F (x) = F d (t) + t to (12), using (14), assuming training error t :…”

Section: Proposed Dmpmentioning

confidence: 99%

“…DMP offer an intuitive training process, as they require one demonstration to learn a desired trajectory, as well as spatial and temporal scaling characteristics. Due to their formulation as dynamical systems they achieve adaptations to uncertainties and perturbations, by being augmented with appropriate coupling terms [10], [11], or with adaptive equations for their parameters [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

A novel DMP formulation for global and frame independent spatial scaling in the task space

Koutras

Doulgeri

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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In this work we study the DMP spatial scaling in the Cartesian space. The DMP framework is claimed to have the ability to generalize learnt trajectories to new initial and goal positions, maintaining the desired kinematic pattern. However we show that the existing formulations present problems in trajectory spatial scaling when used in the Cartesian space for a wide variety of tasks and examine their cause. We then propose a novel formulation alleviating these problems. Trajectory generalization analysis, is performed by deriving the trajectory tracking dynamics. The proposed formulation is compared with the existing ones through simulations and experiments on a KUKA LWR 4+ robot.

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“…At this time, the human manual Stop the robot and start the teach and teach mode to re-teach the recovered motion primitives for the robot to complete the abnormal recovery. Karlsson et al [10,11] proposed an online method of dynamically modifying robot motion primitives to achieve anomaly recovery. This method also pre-parameterized the robot's motion by DMP.…”

Section: Related Workmentioning

confidence: 99%

Learning Policy for Robot Anomaly Recovery Based on Robot Introspection

Zhou

Rojas

et al. 2020

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

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In this chapter, the anomaly recovery would be acted when both of the anomaly monitoring and diagnoses are analysed, which aim to respond the external disturbances from the environmental changes or human intervention in the increasingly human-robot scenarios. To effectively evaluate the exploration, we summarize the anomalies in a robot system include only two catalogues: accidental anomalies and persistent anomalies. In particular, we first diagnose the anomaly as accidental one at the beginning such that the reverse execution is called. If and only if robot reverse many times (not less than twice) and still couldn't avoid or eliminate the anomaly, the human interaction is called. Our proposed system would synchronously record the multimodal sensory signals during the process of human-assisted demonstration. That is, a new movement primitive is learned once an exploring demonstration acquired. Then, we heuristically generate a set of synthetic demonstrations for augmenting the learning by appending a multivariate Gaussian noise distribution with mean equal to zeros and covariance equal to ones. Such that the corresponding introspective capabilities are learned and updated when another human demonstration is acquired. Consequently, incrementally learning the introspective movement primitives with few human corrective demonstrations when an unseen anomaly occurs. It is essential that, although there are only two different exploring strategies when anomaly occurs, numerous exploring behaviors can be generated according to different anomaly types and movement behaviors under various circumstances.

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Convergence of Dynamical Movement Primitives with Temporal Coupling

Cited by 13 publications

References 14 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

A novel DMP formulation for global and frame independent spatial scaling in the task space

Learning Policy for Robot Anomaly Recovery Based on Robot Introspection

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