Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Zeng, Chao; Yang, Chenguang; Cheng, Hong; Li, Yanan; Dai, Shi‐Lu

doi:10.1109/tii.2020.2984482

Cited by 75 publications

(37 citation statements)

References 38 publications

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“…The position DMP formulation can be described as, where the e o is the quaternion error, z is the scaled quaternion error velocity. To obtain the orientation, we solve equation (15),…”

Section: The Composite Dmp Formulationmentioning

confidence: 99%

“…The skill model is expected to be modular, compact and adaptive for robotic manipulation skills. There already exists much work to deal with skill modelling for human-robot skills transfer, such as dynamic movement primitives (DMPs) [9,10], Gaussian mixture model (GMM) [11], the stable estimator of dynamical systems (SEDS) [12], kernelised movement primitives (KMPs) [13], probabilistic movement primitives (ProMPs) [14] and hidden semi-Markov model (HSMM) [15]. And some of these approaches are combined, such as integrating the HSMM with GMM to model the robot skills and perception mechanism [16].…”

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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“…The position DMP formulation can be described as, where the e o is the quaternion error, z is the scaled quaternion error velocity. To obtain the orientation, we solve equation (15),…”

Section: The Composite Dmp Formulationmentioning

confidence: 99%

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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“…Path tracking is an essential link for robots to achieve intelligence and autonomy. Scholars at home and abroad have achieved many research results in robot path tracking control 10‐12 . For example, the pure pursuit method with the advantages of strong practicability and easy implementation was proposed for path tracking 13 .…”

Section: Introductionmentioning

confidence: 99%

Neural networks‐based sliding mode tracking control for the four wheel‐legged robot under uncertain interaction

Wang

2021

Intl J Robust & Nonlinear

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When considering the accuracy of tracking control, physical interaction such as structural uncertainties and external dynamics is the main challenge in actual engineering scenarios, especially for the complex robot system. In this article, a neural network‐based sliding mode tracking control scheme (SMCR) is presented for the developed four wheel‐legged robot (BIT‐NAZA) under the uncertain interaction. First, a non‐singular fast terminal function based on the kinematic model is proposed for path tracking, which improves the motion quality during the approach movement and the sliding mode movement. At the same time, it can reduce the influence of uncertain disturbances on the premise of ensuring the path tracking control accuracy via neural networks. Finally, some demonstrations using the autonomous platform of the BIT‐NAZA robot are employed to evaluate the robustness and effectiveness of the hybrid algorithm.

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“…Conversely, excessive assistance from the robot is not beneficial to promote human users' recovery. These problems motivate researchers towards variable impedance control, aiming to design adaptive impedance approaches to maintain a desired level of pHRI [19,20]. An adaptive impedance controller was developed in [21], where surface electromyography (sEMG) signals were used to obtain the optimal reference impedance parameters for an upper-limb robotic exoskeleton.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning-based path control for robot-assisted upper-limb rehabilitation

Maqsood

Luo

Yang

et al. 2021

Neural Comput & Applic

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In robot-assisted rehabilitation, the performance of robotic assistance is dependent on the human user’s dynamics, which are subject to uncertainties. In order to enhance the rehabilitation performance and in particular to provide a constant level of assistance, we separate the task space into two subspaces where a combined scheme of adaptive impedance control and trajectory learning is developed. Human movement speed can vary from person to person and it cannot be predefined for the robot. Therefore, in the direction of human movement, an iterative trajectory learning approach is developed to update the robot reference according to human movement and to achieve the desired interaction force between the robot and the human user. In the direction normal to the task trajectory, human’s unintentional force may deteriorate the trajectory tracking performance. Therefore, an impedance adaptation method is utilized to compensate for unknown human force and prevent the human user drifting away from the updated robot reference trajectory. The proposed scheme was tested in experiments that emulated three upper-limb rehabilitation modes: zero interaction force, assistive and resistive. Experimental results showed that the desired assistance level could be achieved, despite uncertain human dynamics.

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Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Abstract: (2020) Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning. IEEE Transactions on Industrial Informatics. pp. 1-9.

Cited by 75 publications

References 38 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

Neural networks‐based sliding mode tracking control for the four wheel‐legged robot under uncertain interaction

Iterative learning-based path control for robot-assisted upper-limb rehabilitation

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