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Yinlong Liu

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5 papers

2 author rows

AAMAS Conference 2026 Conference Paper

GCMRBench: Goal-Conditioned Multi-Robot Environments and Benchmarks for Advancing Offline Multi-Agent Reinforcement Learning

Chenxing Li
Chin-jui Chang
Yinlong Liu
Zijian Ma
Jan Seyler
Shahram Eivazi

Research in multi-agent reinforcement learning (MARL) has focused on developing algorithms to address challenges posed by agents’ diverse goals, collaboration, and competition in complex environments. Extending these algorithms to Offline MARL (OMARL), together with the utilization of large-scale offline datasets, has increasingly been recognized as a promising approach toward safe, efficient, and rapid deployment in real-world scenarios. However, most existing studies train and evaluate OMARL in environments primarily designed for game-based scenarios. As a result, the potential of OMARL in domains such as robotics remains an open question. To bridge this gap, we introduce GCMRBench, a goalconditioned multi-agent simulation environment tailored for dualarm robotic tasks and the evaluation of offline multi-agent algorithms, thereby facilitating their deployment in practical robotic applications.

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AAAI Conference 2025 Conference Paper

3SAT: A Simple Self-Supervised Adversarial Training Framework

Jiang Fang
Haonan He
Jiyan Sun
Jiadong Fu
Zhaorui Guo
Yinlong Liu
Wei Ma

The combination of self-supervised learning and adversarial training (AT) can significantly improve the adversarial robustness of self-supervised models. However, the robustness of self-supervised adversarial training (self-AT) still lags behind that of state-of-the-art (SOTA) supervised AT (sup-AT), even though the performance of current self-supervised learning models has already matched or even surpassed that of SOTA supervised learning models. This issue raises concerns about the secure application of self-supervised learning models. The inclusion of adversarial training turns self-AT into a challenging joint optimization problem, and recent studies have shown that the data augmentation methods necessary for constructing positive pairs in self-supervised learning negatively impact the robustness improvement in self-AT. Inspired by this, we propose 3SAT, a simple self-supervised adversarial training framework. 3SAT conducts adversarial training on original, unaugmented samples, reducing the difficulty of optimizing the adversarial training subproblem and fundamentally eliminating the negative impact of data augmentation on robustness improvement. Additionally, 3SAT introduces a dynamic training objective scheduling strategy to address the issue of model training collapse during the joint optimization process when using original samples directly. 3SAT is not only structurally simple and computationally efficient, reducing self-AT training time by half, but it also improves the SOTA self-AT robustness accuracy by 16.19\% and standard accuracy by 11.41\% under Auto-Attack on the CIFAR-10 dataset. Even more impressively, 3SAT surpasses the SOTA sup-AT method in robust accuracy by a significant margin of 11.25\%. This marks the first time that self-AT has outperformed SOTA sup-AT in robustness, indicating that self-AT is a superior method for improving model robustness.

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IROS Conference 2024 Conference Paper

Lightweight Fisheye Object Detection Network with Transformer-based Feature Enhancement for Autonomous Driving

Hu Cao
Yanpeng Li
Yinlong Liu
Xinyi Li
Guang Chen 0001
Alois C. Knoll

Fisheye cameras, offering a wide field of view (FOV) of 360 ◦, are extensively employed for surround-view perception in autonomous driving. Compared with the object detection on the standard images, it lacks studies for fisheye images. Moreover, efficient perception is crucial for autonomous vehicles with limited computational capability. In this work, we introduce a lightweight fisheye object detection network with transformer-based feature enhancement for autonomous driving. Specifically, we leverage ShuffleNet V2 as a feature extraction network to reduce computation complexity and develop a transformer-based feature enhancement module (TFEM) to integrate multi-level features. Notably, we observe that data augmentation methods like mix-up and mosaic, effective on standard images, do not yield positive results on fisheye images. The results on the WoodScape dataset demonstrate that our method can achieve better performance with fewer parameters and floating-point operations per second (FLOPs). Extending our evaluation to the Microsoft Common Objects in Context (MS COCO) dataset shows that the proposed method has excellent generalization capability.

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AAAI Conference 2021 Conference Paper

PointINet: Point Cloud Frame Interpolation Network

Fan Lu
Guang Chen
Sanqing Qu
Zhijun Li
Yinlong Liu
Alois Knoll

LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras. To overcome the temporal limitations of LiDAR sensors, a novel task named Point Cloud Frame Interpolation is studied in this paper. Given two consecutive point cloud frames, Point Cloud Frame Interpolation aims to generate intermediate frame(s) between them. To achieve that, we propose a novel framework, namely Point Cloud Frame Interpolation Network (PointINet). Based on the proposed method, the low frame rate point cloud streams can be upsampled to higher frame rates. We start by estimating bi-directional 3D scene flow between the two point clouds and then warp them to the given time step based on the 3D scene flow. To fuse the two warped frames and generate intermediate point cloud(s), we propose a novel learning-based points fusion module, which simultaneously takes two warped point clouds into consideration. We design both quantitative and qualitative experiments to evaluate the performance of the point cloud frame interpolation method and extensive experiments on two large scale outdoor LiDAR datasets demonstrate the effectiveness of the proposed PointINet. Our code is available at https: //github. com/ispc-lab/PointINet. git.

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NeurIPS Conference 2020 Conference Paper

RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor

Fan Lu
Guang Chen
Yinlong Liu
Zhongnan Qu
Alois Knoll

Keypoint detector and descriptor are two main components of point cloud registration. Previous learning-based keypoint detectors rely on saliency estimation for each point or farthest point sample (FPS) for candidate points selection, which are inefficient and not applicable in large scale scenes. This paper proposes Random Sample-based Keypoint Detector and Descriptor Network (RSKDD-Net) for large scale point cloud registration. The key idea is using random sampling to efficiently select candidate points and using a learning-based method to jointly generate keypoints and corresponding descriptors. To tackle the information loss of random sampling, we exploit a novel random dilation cluster strategy to enlarge the receptive field of each sampled point and an attention mechanism to aggregate the positions and features of neighbor points. Furthermore, we propose a matching loss to train the descriptor in a weakly supervised manner. Extensive experiments on two large scale outdoor LiDAR datasets show that the proposed RSKDD-Net achieves state-of-the-art performance with more than 15 times faster than existing methods. Our code is available at https: //github. com/ispc-lab/RSKDD-Net.

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