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IEEE International Conference on robotics and Automation (ICRA)

作者： Shuai Wang Jiahu Qin Menglin Li Yaonan Wang University of Science and Technology of China Hefei China College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China

Learning driving policies using an end-to-end network has been proved a promising solution for autonomous driving. Due to the lack of a benchmark driver behavior dataset that contains both the visual and the LiDAR data, existing works solely focus on learning driving from visual sensors. Besides, most works are limited to predict steering angle yet neglect the more challenging vehicle speed control problem. In this paper, we propose a novel end-to-end network, FlowDriveNet, which takes advantages of sequential visual data and LiDAR data jointly to predict steering angle and vehicle speed. The main challenges of this problem are how to efficiently extract driving-related information from images and point clouds, and how to fuse them effectively. To tackle these challenges, we propose a concept of point flow and declare that image optical flow and LiDAR point flow are significant motion cues for driving policy learning. Specifically, we first create an enhanced dataset that consists of images, point clouds and corresponding human driver behaviors. Then, in FlowDriveNet, a deep but efficient visual feature extraction module and a point feature extraction module are utilized to extract spatial features from optical flow and point flow, respectively. Additionally, a novel temporal fusion and prediction module is designed to fuse temporal information from the extracted spatial feature sequences and predict vehicle driving commands. Finally, a series of ablation experiments verify the importance of optical flow and point flow and comparison experiments show that our flow-based method outperforms the existing image-based approaches on the task of driving policy learning.

关键词： Image motion analysis Computer vision visualization Laser radar Fuses Predictive models Feature extraction

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation

arXiv

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arXiv 2022年

作者： Liu, Ruiping Yang, Kailun Roitberg, Alina Zhang, Jiaming Peng, Kunyu Liu, Huayao Wang, Yaonan Stiefelhagen, Rainer The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The School of Robotics The National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Artificial Intelligence The University of Stuttgart Stuttgart70569 Germany The Institute for Visual Computing ETH Zurich Zurich8092 Switzerland NIO Shanghai201804 China

Semantic segmentation benchmarks in the realm of autonomous driving are dominated by large pre-trained transformers, yet their widespread adoption is impeded by substantial computational costs and prolonged training durations. To lift this constraint, we look at efficient semantic segmentation from a perspective of comprehensive knowledge distillation and aim to bridge the gap between multi-source knowledge extractions and transformer-specific patch embeddings. We put forward the Transformer-based Knowledge Distillation (TransKD) framework which learns compact student transformers by distilling both feature maps and patch embeddings of large teacher transformers, bypassing the long pre-training process and reducing the FLOPs by >85.0%. Specifically, we propose two fundamental modules to realize feature map distillation and patch embedding distillation, respectively: (1) Cross Selective Fusion (CSF) enables knowledge transfer between cross-stage features via channel attention and feature map distillation within hierarchical transformers;(2) Patch Embedding Alignment (PEA) performs dimensional transformation within the patchifying process to facilitate the patch embedding distillation. Furthermore, we introduce two optimization modules to enhance the patch embedding distillation from different perspectives: (1) Global-Local Context Mixer (GL-Mixer) extracts both global and local information of a representative embedding;(2) Embedding Assistant (EA) acts as an embedding method to seamlessly bridge teacher and student models with the teacher’s number of channels. Experiments on Cityscapes, ACDC, NYUv2, and Pascal VOC2012 datasets show that TransKD outperforms state-of-the-art distillation frameworks and rivals the time-consuming pre-training method. The source code is publicly available at https://***/RuipingL/TransKD. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Domain-invariant Prototypes for Semantic Segmentation

arXiv

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arXiv 2022年

作者： Yang, Zhengeng Yu, Hongshan Sun, Wei Li-Cheng Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu410082 China The Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada The Department of Computer Science The University of Western Australia PerthWA6009 Australia

Deep Learning has greatly advanced the performance of semantic segmentation, however, its success relies on the availability of large amounts of annotated data for training. Hence, many efforts have been devoted to domain adaptive semantic segmentation that focuses on transferring semantic knowledge from a labeled source domain to an unlabeled target domain. Existing self-training methods typically require multiple rounds of training, while another popular framework based on adversarial training is known to be sensitive to hyper-parameters. In this paper, we present an easy-to-train framework that learns domain-invariant prototypes for domain adaptive semantic segmentation. In particular, we show that domain adaptation shares a common character with few-shot learning in that both aim to recognize some types of unseen data with knowledge learned from large amounts of seen data. Thus, we propose a unified framework for domain adaptation and few-shot learning. The core idea is to use the class prototypes extracted from few-shot annotated target images to classify pixels of both source images and target images. Our method involves only one-stage training and does not need to be trained on large-scale un-annotated target images. Moreover, our method can be extended to variants of both domain adaptation and few-shot learning. Experiments on adapting GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes show that our method achieves competitive performance to state-of-the-art. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity

arXiv

引用

arXiv 2023年

作者： Shi, Hao Pang, Chengshan Zhang, Jiaming Yang, Kailun Wu, Yuhao Ni, Huajian Lin, Yining Stiefelhagen, Rainer Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The Department of Engineering Science University of Oxford OxfordOX1 3PJ United Kingdom The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Co. Ltd. Shanghai201210 China

Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehicles and enhances road safety. While previous studies have limitations in using only depth or height information, we find both depth and height matter and they are in fact complementary. The depth feature encompasses precise geometric cues, whereas the height feature is primarily focused on distinguishing between various categories of height intervals, essentially providing semantic context. This insight motivates the development of Complementary-BEV (CoBEV), a novel end-to-end monocular 3D object detection framework that integrates depth and height to construct robust BEV representations. In essence, CoBEV estimates each pixel's depth and height distribution and lifts the camera features into 3D space for lateral fusion using the newly proposed two-stage complementary feature selection (CFS) module. A BEV feature distillation framework is also seamlessly integrated to further enhance the detection accuracy from the prior knowledge of the fusion-modal CoBEV teacher. We conduct extensive experiments on the public 3D detection benchmarks of roadside camera-based DAIR-V2X-I and Rope3D, as well as the private Supremind-Road dataset, demonstrating that CoBEV not only achieves the accuracy of the new state-of-the-art, but also significantly advances the robustness of previous methods in challenging long-distance scenarios and noisy camera disturbance, and enhances generalization by a large margin in heterologous settings with drastic changes in scene and camera parameters. For the first time, the vehicle AP score of a camera model reaches 80% on DAIR-V2X-I in terms of easy mode. The source code will be made publicly available at CoBEV. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

arXiv

引用

arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Learning compliant grasping and manipulation by teleoperation with adaptive force control

arXiv

引用

arXiv 2021年

作者： Zeng, Chao Li, Shuang Jiang, Yiming Li, Qiang Chen, Zhaopeng Yang, Chenguang Zhang, Jianwei Department of Informatics Universiẗat Hamburg Germany National Engineering Laboratory for Robot Visual Perception and Control Hunan University China Center for Cognitive Interaction Technology Bielefeld University Germany Bristol Robotics Laboratory University of the West of England United Kingdom

In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multifingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angle trajectories. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment in a compliant manner. Our approach has been verified in multiple tasks in physics simulation, i.e., grasping, opening-a-door, turning-a-cap, and touching-a-mouse, and has shown more reliable performances than the existing position control and the fixed-gain-based force control approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Remote control

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Edge-Computing-Driven Autonomous Ubiquitous Internet of Things in Electricity: Architecture and Challenges 3

Edge-Computing-Driven Autonomous Ubiquitous Internet of Thin...

引用

3rd IEEE Conference on Energy Internet and Energy System Integration, EI2 2019

作者： Liu, Dongqi Zeng, Xiangjun Wang, Yaonan Changsha Unviersity of Science and Technology Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China

ISBN: (纸本)9781728131375

The distribution network is developing towards the direction of Internet of Things in Electricity(IoTE). As an emerging technology of the Internet of Things(IoT), edge computing has great application potential in the future IoTE. This paper presents an architecture of edge computing-driven IoTE, whose physical layer is an electrical network containing a high proportion of power electronic devices, and its information layer is a 5G-oriented power communication network containing a large amount of power terminal and mass communication data generated by the power terminal. For the physical layer, the virtual synchronous machine(VSM) algorithm are adopted to control the large number of power electronic components so as to guarantee the dynamic stability of physical power network. For the information layer, a new layered distributed information processing architecture, the Edge Computing Architecture, is introduced to address the needs of information monitoring and protection control in the smart distribution network. The architecture of the edge computing, and the applications that may be deployed in the edge computing gateways are introduced, and the challenges may be faced by the IoTE in the future development are further discussed. © 2019 IEEE.

关键词： Internet of things

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Model-free design of stochastic lqr controller from reinforcement learning and primal-dual optimization perspective

arXiv

引用

arXiv 2021年

作者： Li, Man Qin, Jiahu Zheng, Wei Xing Wang, Yaonan Kang, Yu Department of Automation University of Science and Technology of China Hefei230027 China School of Computing Engineering and Mathematics Western Sydney University SydneyNSW2751 Australia College of Electrical and Information Engineering Hunan University Changsha410082 China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha410082 China

To further understand the underlying mechanism of various reinforcement learning (RL) algorithms and also to better use the optimization theory to make further progress in RL, many researchers begin to revisit the linear-quadratic regulator (LQR) problem, whose setting is simple and yet captures the characteristics of RL. Inspired by this, this work is concerned with the model-free design of stochastic LQR controller for linear systems subject to Gaussian noises, from the perspective of both RL and primal-dual optimization. From the RL perspective, we first develop a new model-free off-policy policy iteration (MF-OPPI) algorithm, in which the sampled data is repeatedly used for updating the policy to alleviate the data-hungry problem to some extent. We then provide a rigorous analysis for algorithm convergence by showing that the involved iterations are equivalent to the iterations in the classical policy iteration (PI) algorithm. From the perspective of optimization, we first reformulate the stochastic LQR problem at hand as a constrained non-convex optimization problem, which is shown to have strong duality. Then, to solve this non-convex optimization problem, we propose a model-based primal-dual (MBPD) algorithm based on the properties of the resulting Karush-Kuhn-Tucker (KKT) conditions. We also give a model-free implementation for the MB-PD algorithm by solving a transformed dual feasibility condition. More importantly, we show that the dual and primal update steps in the MB-PD algorithm can be interpreted as the policy evaluation and policy improvement steps in the PI algorithm, respectively. Finally, we provide one simulation example to show the performance of the proposed algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Model

arXiv

引用

arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Zheng, Jin Geng, Zichen Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering China School of Robotics Hunan University Changsha410082 China Central South University Changsha410082 China Lancaster University LA1 4YW United Kingdom The University of Western Australia WA6009 Australia

Object pose estimation is a core means for robots to understand and interact with their environment. For this task, monocular category-level methods are attractive as they require only a single RGB camera. However, current methods rely on shape priors or CAD models of the intra-class known objects. We propose a diffusion-based monocular category-level 9D object pose generation method, MonoDiff9D. Our motivation is to leverage the probabilistic nature of diffusion models to alleviate the need for shape priors, CAD models, or depth sensors for intra-class unknown object pose estimation. We first estimate coarse depth via DINOv2 from the monocular image in a zero-shot manner and convert it into a point cloud. We then fuse the global features of the point cloud with the input image and use the fused features along with the encoded time step to condition MonoDiff9D. Finally, we design a transformer-based denoiser to recover the object pose from Gaussian noise. Extensive experiments on two popular benchmark datasets show that MonoDiff9D achieves state-of-the-art monocular category-level 9D object pose estimation accuracy without the need for shape priors or CAD models at any stage. Our code will be made public at https://***/CNJianLiu/MonoDiff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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