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Weixin Yang

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9 papers

2 author rows

AAAI Conference 2025 Conference Paper

MCGAN: Enhancing GAN Training with Regression-Based Generator Loss

Baoren Xiao
Hao Ni
Weixin Yang

Generative adversarial networks (GANs) have emerged as a powerful tool for generating high-fidelity data. However, the main bottleneck of existing approaches is the lack of supervision on the generator training, which often results in undamped oscillation and unsatisfactory performance. To address this issue, we propose an algorithm called Monte Carlo GAN (MCGAN). This approach, utilizing an innovative generative loss function, termed the regression loss, reformulates the generator training as a regression task and enables the generator training by minimizing the mean squared error between the discriminator's output of real data and the expected discriminator of fake data. We demonstrate the desirable analytic properties of the regression loss, including discriminability and optimality, and show that our method requires a weaker condition on the discriminator for effective generator training. These properties justify the strength of this approach to improve the training stability while retaining the optimality of GAN by leveraging strong supervision of the regression loss. Extensive experiments on diverse datasets, including image data (CIFAR-10/100, FFHQ256, ImageNet, and LSUN Bedroom), time series data (VAR and stock data), and video data, are conducted to demonstrate the flexibility and effectiveness of our proposed MCGAN. Numerical results show that the proposed MCGAN is versatile in enhancing a variety of backbone GAN models and achieves consistent and significant improvement in terms of quality, accuracy, training stability, and learned latent space.

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ICRA Conference 2021 Conference Paper

Morphologically Adapatative Quad-Rotor Towards Acquiring High-Performance Flight: A Comparative Study and Validation

Na Zhao 0008
Weixin Yang
Cong Peng 0006
Gang Wang 0024
Yantao Shen 0001

This paper presents our comparative study on how the flight performances of an in-flight morphing quad-rotor are affected by the morph induced inertia variation. A custom-built in-flight morphing quad-rotor was employed in numerical and experimental tests for the study and analysis. In these tests, the quad-rotor is controlled to follow a predefined path and/or to hover in an environment with the constant wind disturbance. Our numerical results indicate that the morphing-size-down quad-rotor exhibits more agile in flight attributed to the compact volume/size, while the big-size one shows more flight stability in a disturbed and windy environment. Compared to regular scaled aerial vehicles whose volume/size changes follow a weight change proportionally, the numerical results reveal that our morphing quad-rotor that changes its volume with identical mass can acquire more merits towards high flight performances. Experimental validations further prove that through adaptatively transform its size in a complex and constrained environment, the in-flight morphing quad-rotor is not only capable of well performing path following tasks when encountering obstacles on the path or nearby the path, but also enhances the flight performance of withstanding external torques by extending its size so as to increase its moment-of-inertia. In summary, our in-flight morphing quad-rotor can acquire higher flight performances by adaptatively morphing when flying in complex environments.

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ICRA Conference 2020 Conference Paper

Distributed Consensus Control of Multiple UAVs in a Constrained Environment

Gang Wang 0024
Weixin Yang
Na Zhao 0008
Yunfeng Ji
Yantao Shen 0001
Hao Xu 0002
Peng Li 0019

In this paper, we investigate the consensus problem of multiple unmanned aerial vehicles (UAVs) in the presence of environmental constraints under a general communication topology containing a directed spanning tree. First, based on a position transformation function, we propose a novel dynamic reference position and yaw angle for each UAV to cope with both the asymmetric topology and the constraints. Then, the backstepping-like design methodology is presented to derive a local tracking controller for each UAV such that its position and yaw angle can converge to the reference ones. The proposed protocol is distributed in the sense that, the input update of each UAV dynamically relies only on local state information from its neighborhood set and the constraints, and it does not require any additional centralized information. It is demonstrated that under the proposed protocol, all UAVs reach consensus without violation of the environmental constraints. Finally, simulation and experimental results are provided to demonstrate the performance of the protocol.

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IROS Conference 2020 Conference Paper

Perception-Aware Path Finding and Following of Snake Robot in Unknown Environment

Weixin Yang
Gang Wang 0024
Yantao Shen 0001

In this paper, we investigate the perception-aware path finding, planning and following for a class of snake robots autonomously serpentining in an unmodeled and unknown environment. In the work, the onboard LiDAR sensor mounted on the head of the snake robot is utilized to reconstruct the local environment, by which and the modified rapidly-exploring random tree method, a feasible path from the current position of the robot to a local selected target position can be obtained. Next, the parametric cubic spline interpolation path-planning method and potential functions are applied to make the path more smooth so as to prevent the multi-link and elongated robot body from hitting obstacles. To steer, a time-varying line-of-sight control law is designed to ensure that the robot moves to the local target position along the generated path by the perception-aware method. The robot will repeatedly perform the above search-find-move strategy until it reaches the final predefined target point. Simulation and experimental results demonstrate a good performance of the proposed perception-aware approach, that is, the elongated and underactuated snake robot is capable of autonomously navigating in an unknown environment.

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IROS Conference 2019 Conference Paper

An Approximation-Free Simple Control Scheme for Uncertain Quadrotor Systems: Theory and Validations

Gang Wang 0024
Weixin Yang
Na Zhao 0008
Peng Li 0019
Yantao Shen 0001
Chaoli Wang 0002

In this paper, a simple tracking control scheme is proposed for quadrotor systems with uncertain dynamics. It precludes the necessity for prohibitive analytic computation of the derivatives of the desired (virtual) attitude that is typically employed in controlling quadrotor systems. Moreover, this control scheme is approximation-free in the sense that it does not incorporate any adaptive laws, observers, or command filters to compensate for unknown parameters in the dynamics and the absence of the analytic differentiation, thus exhibiting remarkably low complexity levels and making its implementation straightforward. The thrust saturation is approached in the position control design which also enables the singularity in desired attitude extraction to be avoided entirely. It is demonstrated that based on the proposed scheme, the tracking errors can be made arbitrarily small by appropriately selecting design parameters. Extensive simulations and experiments are performed to verify the effectiveness of our scheme.

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AAAI Conference 2019 Conference Paper

Skeleton-Based Gesture Recognition Using Several Fully Connected Layers with Path Signature Features and Temporal Transformer Module

Chenyang Li
Xin Zhang
Lufan Liao
Lianwen Jin
Weixin Yang

The skeleton based gesture recognition is gaining more popularity due to its wide possible applications. The key issues are how to extract discriminative features and how to design the classification model. In this paper, we first leverage a robust feature descriptor, path signature (PS), and propose three PS features to explicitly represent the spatial and temporal motion characteristics, i. e. , spatial PS (S PS), temporal PS (T PS) and temporal spatial PS (T S PS). Considering the significance of fine hand movements in the gesture, we propose an ”attention on hand” (AOH) principle to define joint pairs for the S PS and select single joint for the T PS. In addition, the dyadic method is employed to extract the T PS and T S PS features that encode global and local temporal dynamics in the motion. Secondly, without the recurrent strategy, the classification model still faces challenges on temporal variation among different sequences. We propose a new temporal transformer module (TTM) that can match the sequence key frames by learning the temporal shifting parameter for each input. This is a learning-based module that can be included into standard neural network architecture. Finally, we design a multi-stream fully connected layer based network to treat spatial and temporal features separately and fused them together for the final result. We have tested our method on three benchmark gesture datasets, i. e. , ChaLearn 2016, ChaLearn 2013 and MSRC-12. Experimental results demonstrate that we achieve the state-of-the-art performance on skeleton-based gesture recognition with high computational efficiency.

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ICRA Conference 2019 Conference Paper

Spline Based Curve Path Following of Underactuated Snake Robots

Weixin Yang
Gang Wang 0024
Haiyan Shao
Yantao Shen 0001

This paper investigates the curve path following problem for a class of planar underactuated bio-inspired snake robots. The time-varying line-of-sight (LOS) guidance law and the cubic spline interpolation (CSI) path-planning method are employed. Existing studies focus on straight line path following which only gives a solution for snake robot motion control in relatively simple environments. Considering the snake robot's many degrees of freedom and excellent mobility in terrains, we propose a more applicable solution of curve path following for snake robots on the ground. The improved LOS helps the snake robot to steer aggressively at a sharp turning point. Furthermore, to avoid the sideslip of the snake robot caused by the ground friction change, an integral controller is introduced in the design of the heading reference. Simulations and experiments on an 8-link custom-built snake robot are conducted and the results demonstrate and validate the effectiveness of the proposed curve path following algorithm.

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IROS Conference 2018 Conference Paper

Adaptive Path Following of Snake Robot on Ground with Unknown and Varied Friction Coefficients

Gang Wang 0024
Weixin Yang
Yantao Shen 0001
Haiyan Shao

This paper investigates the straight path following problem for a class of underactuated bio-inspired snake robots on ground with unknown and varied friction coefficients. Existing works usually design control input requiring the exact values of these friction coefficients, which however rely on the specific operating terrain and may not always be known a priori. By virtue of backstepping technique, we present a novel adaptive controller that can compensate for unknown and varied friction coefficients in real-time. Moreover, it is proved via LaSalle-Yoshizawa theorem that the path following errors converge to zero asymptotically and all the parameter estimates are bounded. Simulations and experiments on an 8-link snake robot are carried out to illustrate the effectiveness of the proposed controller.

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IS Journal 2016 Journal Article

DeepWriterID: An End-to-End Online Text-Independent Writer Identification System

Weixin Yang
Lianwen Jin
Manfei Liu

The rapid adoption of touchscreen mobile terminals and pen-based interfaces has increased the demand for handwriting-based writer identification systems, particularly in the areas personal authentication and digital forensics. However, most writer identification systems yield poor performance because of insufficient data and an inability to handle the various conditions inherent in handwriting samples. To address these problems, the authors introduce the end-to-end DeepWriterID system that employs a deep convolutional neural network (CNN) and incorporates a new method called DropSegment to achieve data augmentation and improve the generalized applicability of CNN. Experiments show DeepWriterID achieves accuracy rates of 95. 72 percent for Chinese text and 98. 51 percent for English text.

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