A Control Strategy of Robot Eye-Head Coordinated Gaze Behavior Achieved for Minimized Neural Transmission Noise

Liu, Xiaorui; Jiang, Wanyue; Su, Hang; Qi, Wen; Ge, Shuzhi Sam

doi:10.1109/tmech.2022.3210592

Cited by 18 publications

(15 citation statements)

References 40 publications

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“…The subjects were asked to hold the handle to move according to the desired trajectory in every training trial. He/She made movement corrections in response to visual feedback and force feedback [37, 38]. After each trial, the posterior distribution of the subject’s performance to the hyperparameter was generated by the Gaussian process based on historical hyperparameters and performances.…”

Section: Methodsmentioning

confidence: 99%

Bayesian optimization for assist-as-needed controller in robot-assisted upper limb training based on energy information

Zhang

Zeng

et al. 2023

Robotica

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The assist-as-needed (AAN) controller is effective in robot-assisted rehabilitation. However, variations of the engagement of subjects with fixed controller often lead to unsatisfying results. Therefore, adaptive AAN that adjusts control parameters based on individualized engagement is essential to enhance the training effect further. Nevertheless, current approaches mainly focus on the within-trial real-time engagement estimation, and the presence of measurement noise may cause improper evaluation of engagement. In addition, most studies on human-in-loop optimization strategies modulate the controller by greedy strategies, which are prone to fall into local optima. These shortcomings in previous studies could significantly limit the efficacy of AAN. This paper proposes an adaptive AAN to promote engagement by providing subjects with a subject-adaptive assistance level based on trial-wise engagement estimation and performance. Firstly, the engagement is estimated from energy information, which assesses the work done by the subject during a full trial to reduce the influence of measurement outliers. Secondly, the AAN controller is adapted by Bayesian optimization (BO) to maximize the subject’s performance according to historical trial-wise performance. The BO algorithm is good at dealing with noisy signals within limited steps. Experiments with ten healthy subjects resulted in a decrease of 34.59 $\%$ in their average trajectory error, accompanied by a reduction of 9.71 $\%$ in their energy consumption, thus verifying the superiority of the proposed method to prior work. These results suggest that the proposed method could potentially improve the effect of upper limb rehabilitation.

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Section: Methodsmentioning

confidence: 99%

Bayesian optimization for assist-as-needed controller in robot-assisted upper limb training based on energy information

Zhang

Zeng

et al. 2023

Robotica

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“…With the development of robot application and the combination of deep learning technology and robots ( Qi and Su, 2022 ), robots are increasingly intelligent ( Chen and Qiao, 2020b , Wang et al, 2022 ) and behaving more and more like human beings ( Su et al, 2022b ). Now robots can be driven by humans to display natural and appropriate behaviors in social scenes ( Liu et al, 2022a ). Cylinder-aperture ESM measurement system sees the successful application of a 6-DOF manipulator ( Liu et al, 2022b ).…”

Section: Our Choicementioning

confidence: 99%

Actuation delay compensation of robots in semi-physical test

Zhang

He²,

Xu³

et al. 2023

Front. Neurorobot.

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In general, the traditional spacecraft semi-physical docking tests include the evaluation of docking and separation performance. However, these tests often rely on “specific” equipment, such as specially designed actuators and fast-response hydraulic systems, to meet the stringent dynamic response requirements of semi-physical testing. In this paper, a novel docking test platform is designed based on a general-purpose industrial manipulator using 3-D force and 3-D torque sensors. Different from the traditional solution, this novel platform is well-assembled and cost-effective. Furthermore, an actuation delay compensation method is introduced to improve the performance. Finally, the proposed method is evaluated using simulations. The results show that the novel method is with promising performance in terms of actuation delay compensation.

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“…Deep learning-related technologies are increasingly integrated into people’s daily life, and object detection algorithms ( Qi et al, 2021 ; Liu et al, 2022a , b ; Xu et al, 2022 ), as a crucial component of the autonomous driving perception layer, can create a solid foundation for behavioral judgments during autonomous driving. Although object detection algorithms based on 2D images ( Bochkovskiy et al, 2020 ; Bai et al, 2022 ; Cheon et al, 2022 ; Gromada et al, 2022 ; Long et al, 2022 ; Otgonbold et al, 2022 ; Wahab et al, 2022 ; Wang et al, 2022 ) have had a lot of success at this stage, single-view images cannot completely reflect the position pose, and motion orientation of objects in 3D space due to the lack of depth information in 2D images.…”

Section: Introductionmentioning

confidence: 99%

Efficient three-dimensional point cloud object detection based on improved Complex-YOLO

Yongxin

Zhetao²,

Tan³

et al. 2023

Front. Neurorobot.

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Lidar-based 3D object detection and classification is a critical task for autonomous driving. However, inferencing from exceedingly sparse 3D data in real-time is a formidable challenge. Complex-YOLO solves the problem of point cloud disorder and sparsity by projecting it onto the bird’s-eye view and realizes real-time 3D object detection based on LiDAR. However, Complex-YOLO has no object height detection, a shallow network depth, and poor small-size object detection accuracy. To address these issues, this paper has made the following improvements: (1) adds a multi-scale feature fusion network to improve the algorithm’s capability to detect small-size objects; (2) uses a more advanced RepVGG as the backbone network to improve network depth and overall detection performance; and (3) adds an effective height detector to the network to improve the height detection. Through experiments, we found that our algorithm’s accuracy achieved good performance on the KITTI dataset, while the detection speed and memory usage were very superior, 48FPS on RTX3070Ti and 20FPS on GTX1060, with a memory usage of 841Mib.

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A Control Strategy of Robot Eye-Head Coordinated Gaze Behavior Achieved for Minimized Neural Transmission Noise

Cited by 18 publications

References 40 publications

Bayesian optimization for assist-as-needed controller in robot-assisted upper limb training based on energy information

Bayesian optimization for assist-as-needed controller in robot-assisted upper limb training based on energy information

Actuation delay compensation of robots in semi-physical test

Efficient three-dimensional point cloud object detection based on improved Complex-YOLO

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