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Ruoyu Xu

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8 papers

2 author rows

AAAI Conference 2025 Conference Paper

SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection

Ruoyu Xu
Zhiyu Xiang
Chenwei Zhang
Hanzhi Zhong
Xijun Zhao
Ruina Dang
Peng Xu
Tianyu Pu

3D object detection is one of the fundamental perception tasks for autonomous vehicles. Fulfilling such a task with a 4D millimeter-wave radar is very attractive since the sensor is able to acquire 3D point clouds similar to Lidar while maintaining robust measurements under adverse weather. However, due to the high sparsity and noise associated with the radar point clouds, the performance of the existing methods is still much lower than expected. In this paper, we propose a novel Semi-supervised Cross-modality Knowledge Distillation (SCKD) method for 4D radar-based 3D object detection. It characterizes the capability of learning the feature from a Lidar-radar-fused teacher network with semi-supervised distillation. We first propose an adaptive fusion module in the teacher network to boost its performance. Then, two feature distillation modules are designed to facilitate the cross-modality knowledge transfer. Finally, a semi-supervised output distillation is proposed to increase the effectiveness and flexibility of the distillation framework. With the same network structure, our radar-only student trained by SCKD boosts the mAP by 10.38% over the baseline and outperforms the state-of-the-art works on the VoD dataset. The experiment on ZJUODset also shows 5.12% mAP improvements on the moderate difficulty level over the baseline when extra unlabeled data are available.

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AAAI Conference 2024 Short Paper

Bridging the Gap between Source Code and Requirements Using GPT (Student Abstract)

Ruoyu Xu
Zhenyu Xu
Gaoxiang Li
Victor S. Sheng

Reverse engineering involves analyzing the design, architecture, and functionality of systems, and is crucial for legacy systems. Legacy systems are outdated software systems that are still in use and often lack proper documentation, which makes their maintenance and evolution challenging. To address this, we introduce SC2Req, utilizing the Generative Pre-trained Transformer (GPT) for automated code analysis and requirement generation. This approach aims to convert source code into understandable requirements and bridge the gap between those two. Through experiments on diverse software projects, SC2Req shows the potential to enhance the accuracy and efficiency of the translation process. This approach not only facilitates faster software development and easier maintenance of legacy systems but also lays a strong foundation for future research, promoting better understanding and communication in software development.

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AAAI Conference 2024 Short Paper

ChatGPT-Generated Code Assignment Detection Using Perplexity of Large Language Models (Student Abstract)

Zhenyu Xu
Ruoyu Xu
Victor S. Sheng

In the era of large language models like Chatgpt, maintaining academic integrity in programming education has become challenging due to potential misuse. There's a pressing need for reliable detectors to identify Chatgpt-generated code. While previous studies have tackled model-generated text detection, identifying such code remains uncharted territory. In this paper, we introduce a novel method to discern Chatgpt-generated code. We employ targeted masking perturbation, emphasizing code sections with high perplexity. Fine-tuned CodeBERT is utilized to replace these masked sections, generating subtly perturbed samples. Our scoring system amalgamates overall perplexity, variations in code line perplexity, and burstiness. In this scoring scheme, a higher rank for the original code suggests it's more likely to be chatgpt-generated. The underlying principle is that code generated by models typically exhibits consistent, low perplexity and reduced burstiness, with its ranking remaining relatively stable even after subtle modifications. In contrast, human-written code, when perturbed, is more likely to produce samples that the model prefers. Our approach significantly outperforms current detectors, especially against OpenAI's text-davinci-003 model, with the average AUC rising from 0.56 (GPTZero baseline) to 0.87.

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AAAI Conference 2023 Short Paper

ACCD: An Adaptive Clustering-Based Collusion Detector in Crowdsourcing (Student Abstract)

Ruoyu Xu
Gaoxiang Li
Wei Jin
Austin Chen
Victor S. Sheng

Crowdsourcing is a popular method for crowd workers to collaborate on tasks. However, workers coordinate and share answers during the crowdsourcing process. The term for this is "collusion". Copies from others and repeated submissions are detrimental to the quality of the assignments. The majority of the existing research on collusion detection is limited to ground truth problems (e.g., labeling tasks) and requires a predetermined threshold to be established in advance. In this paper, we aim to detect collusion behavior of workers in an adaptive way, and propose an Adaptive Clustering Based Collusion Detection approach (ACCD) for a broad range of task types and data types solved via crowdsourcing (e.g., continuous rating with or without distributions). Extensive experiments on both real-world and synthetic datasets show the superiority of ACCD over state-of-the-art approaches.

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ICRA Conference 2022 Conference Paper

Design and Optimization of a Magnetic Catcher for UAV Landing on Disturbed Aquatic Surface Platforms

Chongfeng Liu
Zixing Jiang
Ruoyu Xu
Xiaoqiang Ji 0001
Lianxin Zhang
Huihuan Qian

In this paper, a new capture system for UAV precision landing in a disturbed environment is proposed. Compared with the traditional visual guided landing methods, perching mechanism based methods, and tethered landing methods, the proposed system takes into account the stability during landing process and retains the high accessibility of the UAV. The proposed system consists of a winch subsystem and a magnetic catcher device. They establish an automatic tethered-UAV system for landing before the UAV touchdown. We analyzed the design principle as well as the feasibility of the magnetic catcher. An optimization problem is formulated to obtain a better layout of magnets on the catcher. The problem is relaxed based on interpolation simulation of attraction force. Experiments are conducted both in indoor and outdoor environments based on different UAV platforms respectively. The results validate that the catcher design and the capture system can achieve a successful landing in both cases.

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IROS Conference 2021 Conference Paper

A Predictive Control Method for Stabilizing a Manipulator-based UAV Landing Platform on Fluctuating Marine Surface

Ruoyu Xu
Xiaoqiang Ji 0001
Jiafan Hou
Hengli Liu
Huihuan Qian

In the process of landing unmanned aerial vehicles (UAVs) on an unmanned surface vehicle (USV), a manipulator can be applied to help the UAV land safely and accurately. However, it is a challenge to control the manipulator on a disturbed USV due to joint velocity constraints and bandwidth limitations. To solve this problem, a predictive control framework is proposed in this paper. We leverage a first-order delay system to describe the kinematics of each joint, and control joint velocities by the model predictive controller (MPC). To generate references for MPC, the motion of the floating base needs to be predicted. We apply the recent approach for motion prediction based on the wavelet network (WN) and modify the network to get smooth trajectories. The accuracy of the modified wavelet network (MWN) for motion prediction is tested on four-hour motion data from the real ocean environment and the smoothness of the generated trajectories is also evaluated. Simulations and experiments are implemented to verify the proposed method, the results show that the average control accuracies are improved by more than 30% and 50% in position and rotation compared with the traditional inverse kinematics (IK) controller for 1 Hz base fluctuation.

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ICRA Conference 2020 Conference Paper

A Novel Solar Tracker Driven by Waves: From Idea to Implementation

Ruoyu Xu
Hengli Liu
Chongfeng Liu
Zhenglong Sun 0001
Tin Lun Lam
Huihuan Qian

Traditional solar trackers often adopt motors to automatically adjust the attitude of the solar panels towards the sun for maximum power efficiency. In this paper, a novel design of solar tracker for the ocean environment is introduced. Utilizing the fluctuations due to the waves, electromagnetic brakes are utilized instead of motors to adjust the attitude of the solar panels. Compared with the traditional solar trackers, the proposed one is simpler in hardware while the harvesting efficiency is similar. The desired attitude is calculated out of the local location and time. Then based on the dynamic model of the system, the angular acceleration of the solar panels is estimated and a control algorithm is proposed to decide the release and lock states of the brakes. In such a manner, the adjustment of the attitude of the solar panels can be achieved by using two brakes only. Experiments are conducted to validate the acceleration estimator and the dynamic model. At last, the feasibility of the proposed solar tracker is tested on the real water surface. The results show that the system is able to adjust 40° in two dimensions within 28 seconds.

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IROS Conference 2020 Conference Paper

A Two-stage Automatic Latching System for The USVs Charging in Disturbed Berth

Kaiwen Xue
Chongfeng Liu
Hengli Liu
Ruoyu Xu
Zhenglong Sun 0001
Tin Lun Lam
Huihuan Qian

Automatic latching for charging in a disturbed environment for Unmanned Surface Vehicle (USVs) is always a challenging problem. In this paper, we propose a two-stage automatic latching system for USVs charging in berth. In Stage I, a vision-guided algorithm is developed to calculate an optimal latching position for charging. In Stage II, a novel latching mechanism is designed to compensate the movement misalignments from the water disturbance. A set of experiments have been conducted in real-world environments. The results show the latching success rate has been improved from 40% to 73. 3% in the best cases with our proposed system. Furthermore, the vision-guided algorithm provides a methodology to optimize the design radius of the latching mechanism with respect to different disturbance levels accordingly. Outdoor experiments have validated the efficiency of our proposed automatic latching system. The proposed system improves the autonomy intelligence of the USVs and provides great benefits for practical applications.

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