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MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language models using 2D Priors 24

MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large ...

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32nd ACM international conference on Multimedia, MM 2024

作者： Tang, Yuan Han, Xu Li, Xianzhi Yu, Qiao Hao, Yixue Hu, Long Chen, Min Huazhong University of Science and Technology Wuhan China South China University of Technology Guangzhou China

ISBN: (纸本)9798400706868

Large 2D vision-language models (2D-LLMs) have gained significant attention by bridging Large Language models (LLMs) with images using a simple projector. Inspired by their success, large 3D point cloud-language models (3D-LLMs) also integrate point clouds into LLMs. However, directly aligning point clouds with LLM requires expensive training costs, typically in hundreds of GPU-hours on A100, which hinders the development of 3D-LLMs. In this paper, we introduce MiniGPT-3D, an efficient and powerful 3D-LLM that achieves multiple SOTA results while training for only 27 hours on one RTX 3090. Specifically, we propose to align 3D point clouds with LLMs using 2D priors from 2D-LLMs, which can leverage the similarity between 2D and 3D visual information. We introduce a novel four-stage training strategy for modality alignment in a cascaded way, and a mixture of query experts module to adaptively aggregate features with high efficiency. Moreover, we utilize parameter-efficient fine-tuning methods LoRA and Norm fine-tuning, resulting in only 47.8M learnable parameters, which is up to 260x fewer than existing methods. Extensive experiments show that MiniGPT-3D achieves SOTA on 3D object classification and captioning tasks, with significantly cheaper training costs. Notably, MiniGPT-3D gains an 8.12 increase on GPT-4 evaluation score for the challenging object captioning task compared to ShapeLLM-13B, while the latter costs 160 total GPU-hours on 8 A800. We are the first to explore the efficient 3D-LLM, offering new insights to the community. Code and weights are available at https://***/TangYuan96/MiniGPT-3D. © 2024 ACM.

关键词： Visual languages

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Constrained Passive Interaction Control: Leveraging Passivity and Safety for Robot Manipulators

Constrained Passive Interaction Control: Leveraging Passivit...

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IEEE international conference on robotics and automation (ICRA)

作者： Zhiquan Zhang Tianyu Li Nadia Figueroa School of Engineering and Applied Science University of Pennsylvania Pennsylvania PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Passivity is necessary for robots to fluidly collaborate and interact with humans physically. Nevertheless, due to the unconstrained nature of passivity-based impedance control laws, the robot is vulnerable to infeasible and unsafe configurations upon physical perturbations. In this paper, we propose a novel control architecture that allows a torque-controlled robot to guarantee safety constraints such as kinematic limits, self-collisions, external collisions and singularities and is passive only when feasible. This is achieved by constraining a dynamical system based impedance control law with a relaxed hierarchical control barrier function quadratic program subject to multiple concurrent, possibly contradicting, constraints. Joint space constraints are formulated from efficient data-driven self- and external ${\mathcal{C}^2}$ collision boundary functions. We theoretically prove constraint satisfaction and show that the robot is passive when feasible. Our approach is validated in simulation and real robot experiments on a 7DoF Franka Research 3 manipulator.

关键词： Perturbation methods Kinematics Aerospace electronics Manipulators Control systems Safety Impedance

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Research on the Power Control of MMC-PET System Based on VSG and PBC 7

Research on the Power Control of MMC-PET System Based on VSG...

引用

7th IEEE international Electrical and Energy conference, CIEEC 2024

作者： Li, Huizhu Xia, Yan Chen, Yang Yang, Kai Sichuan University of Science & Engineering School of Automation and Information Engineering Yibin China

ISBN: (纸本)9798350359558

Power Electronic Transformer (PET) is a new type of power electronic device with flexible topology access, which has a wide range of application prospects in AC and DC microgrids. For the control of the MMC-PET system, which is cascaded by a modular multilevel converter (MMC) and dual active bridge (DAB), the traditional PI control method lacks damping inertia, has poor dynamic performance, and is unable to modulate the frequency actively. This paper proposes a current and voltage control technique based on virtual synchronous generators (VSG) theory and passivity-based control (PBC) theory. Firstly, the basic topology and working principle of MMC-PET are introduced, and the mathematical models of MMC and DAB are analyzed. Secondly, the passivity of MMC is investigated, and the passive controller is combined with the VSG control with primary FM to optimize the MMC-PET control so that the MMC-PET system has the function of primary FM with better robustness. Finally, the MMC-PET system model is constructed in Matlab/Simulink. The simulation experiments of the above VSG and PBC control methods are carried out under the working conditions of frequency fluctuation and load change in the power grid. The results show the feasibility and effectiveness of the proposed MMC-PET system model and control method. © 2024 IEEE.

关键词： Topology

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Point Cloud based Motion State Estimation Method for Autonomous Driving

Point Cloud based Motion State Estimation Method for Autonom...

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international conference on Advanced Mechatronic Systems (ICAMechS)

作者： Tao Jiang Enzhi Zhu Sidong Wu Linshuai Zhang Shuoxin Gu Lin Xu Qian Wang Xianggui Tang School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu

Generally, motion vector information primarily stems from moving objects, while static objects contribute minimally to the estimation task. Traditional approaches for motion vector estimation typically rely on scene flow methods that depend on deep models to extract features from individual points at a high cost. These methods then acquire flow information through complex matching mechanisms or feature decoding. Such approaches are computationally expensive and exhibit substantial latency. Moreover, they neglect the importance of motion objects in motion vector estimation and the interference from static objects. Therefore, this paper introduces a novel method that first performs point cloud motion segmentation and subsequently estimates motion vectors. This approach focuses on leveraging point cloud information annotated with motion objects to estimate three-dimensional scene flow more effectively. By employing motion segmentation, we can obtain annotations for moving objects, enabling greater emphasis on the estimation of motion vectors for more challenging cases. In our experiments conducted on the KITTI dataset, the proposed method demonstrates superior performance compared to existing scene flow estimation methods. Specifically, without considering motion segmentation errors, the error in the motion direction is only 0.0363 m/s, showcasing better performance. Additionally, our method achieves an error of 0.076 m for three-dimensional endpoint error (EPE3D), showcasing distinct advantages over current scene flow networks.

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SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images

SyreaNet: A Physically Guided Underwater Image Enhancement F...

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IEEE international conference on robotics and automation (ICRA)

作者： Junjie Wen Jinqiang Cui Zhenjun Zhao Ruixin Yan Zhi Gao Lihua Dou Ben M. Chen Department of Mechanical and Automation Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Mathematics and Theories Peng Cheng Laboratory Shen-zhen China School of Remote Sensing and Information Engineering Wuhan University Wuhan China School of Automation Beijing Institute of Technology Beijing China

Underwater image enhancement (UIE) is vital for high-level vision-related underwater tasks. Although learning-based UIE methods have made remarkable achievements in recent years, it's still challenging for them to consistently deal with various underwater conditions, which could be caused by: 1) the use of the simplified atmospheric image formation model in UIE may result in severe errors; 2) the network trained solely with synthetic images might have difficulty in generalizing well to real underwater images. In this work, we, for the first time, propose a framework SyreaNet for UIE that integrates both synthetic and real data under the guidance of the revised underwater image formation model and novel domain adaptation (DA) strategies. First, an underwater image synthesis module based on the revised model is proposed. Then, a physically guided disentangled network is designed to predict the clear images by combining both synthetic and real underwater images. The intra- and inter-domain gaps are abridged by fully exchanging the domain knowledge. Extensive experiments demonstrate the superiority of our framework over other state-of-the-art (SOTA) learning-based UIE methods qualitatively and quantitatively. The code and dataset are publicly available at https://***/RockWenJJ/***.

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A Seismic Phase-Picking Model Transfer Learning Approach Based on Maximum Mean Discrepancy 3

A Seismic Phase-Picking Model Transfer Learning Approach Bas...

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3rd international conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Zhu, Kun'ao Huang, Yongming Chen, Jun Sun, Yejun School of Automation Southeast University Nanjing China School of Electronic Information and Electrical Engineering Hefei Normal University Hefei China Jiangsu Earthquake Agency Nanjing China

ISBN: (纸本)9798350372052

Thanks to the abundance of earthquake data and the advancement of deep neural networks, many seismic phase-picking models have been proposed to detect the arrival time of P-waves and S-waves. Currently, the accuracy of these models exceeds that of traditional methods, such as short-term average/long-term average (STA/LTA). While many studies concentrate on phase picks on a single seismic dataset, there is limited research regarding the generalization of phase-picking models. Specifically, applying a model trained on one seismic dataset to an unseen dataset remains underexplored. A phase-picking model is designed in this paper, which consists of an encoder and a decoder. The self-attention is applied in the encoder module to focus seismic features. Then a seismic phase-picking model transfer learning approach based on maximum mean discrepancy (SPPTL-MMD) is proposed to train a universal seismic feature extraction module by utilizing diverse seismic datasets. The module will be transferred and a significantly improved phase-picking model is obtained through fine-tuning techniques. Experiments show that the model after fine-tuning can be generalized to other datasets. © 2023 IEEE.

关键词： Signal encoding

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Application of Predictive Algorithms in Informational Learning 2

Application of Predictive Algorithms in Informational Learni...

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2nd international conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2023

作者： Han, Xue Hou, Guanyu Jilin Agricultural Science and Technology University Jilin Editorial Department of Journal Jilin China Harbin Institute of Technology School of Mechanical and Electrical Engineering and Automation Guangdong Shenzhen China

ISBN: (纸本)9798350339765

The application of predictive algorithms has penetrated into various fields, and their applications are becoming increasingly widespread in information learning. The popularization of online learning models and methods allows learners to utilize various and diverse learning resources, develop their knowledge and skills, and grow rapidly in learning. However, the rapid increase in learning resources and information has also brought huge challenges to learning, requiring more efficient learning methods. At this time, predictive algorithms have emerged. Predictive algorithms can analyze and evaluate learners' historical learning records and learning behavior patterns, predict learners' learning interests and needs, and recommend personalized learning resources. In this way, predictive algorithms provide efficient and accurate learning suggestions for students, helping to improve learning efficiency and outcomes. In the experimental data, 2.7% were very satisfied with the use of traditional information-based learning systems, and 98.2% were very satisfied with the use of deep learning prediction algorithms in information-based learning systems, indicating a significant improvement. Therefore, predictive algorithms have become an indispensable and important component in information learning. © 2023 IEEE.

关键词： Learning systems

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A Novel Variable Resolution Torque Sensor Based on Variable Stiffness Principle

A Novel Variable Resolution Torque Sensor Based on Variable ...

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IEEE international conference on robotics and automation (ICRA)

作者： Sun, Xiantao Chen, Wenjie Zhang, Jianbin Wang, Jianhua Jiang, Jun Chen, Weihai Anhui Univ Sch Elect Engn & Automat Anhui Engn Lab Human Machine Cooperat Syst & Inte Hefei Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Beijing Inst Control Engn Beijing Peoples R China

ISBN: (纸本)9781728190778

High resolution and large range force/torque (F/T) measurements are usually required in many engineering tasks. However, most existing F/T sensors only have a fixed resolution over their whole ranges. The key lies in that it is difficult to well balance high resolution and large range in the sensor design. Taking the torque sensor for example, this paper presents a better compromise for this problem i.e., a novel variable resolution torque sensor based on variable stiffness principle. From the structural points of view, the sensor is constructed with multiple radial flexures to achieve a pure rotational motion with negligible parasitic center motions. Two resistive strain gauges (RSGs) are selected as the measuring units of the sensor to detect the applied external torque and meanwhile provide variable resolutions in the two different measuring ranges (each RSG for one range). Static and dynamic models of the sensor are established in details and validated through finite element analysis (FEA) to evaluate its characteristics. A principle prototype is finally fabricated and tested to verify the effectiveness of the presented design. RSGs are calibrated through a commercial six-axis F/T sensor from ATI Industrial automation, Inc. Experimental results show that the torque sensor can provide high and low resolutions in the small and large ranges respectively and possesses the first natural frequency of 673 Hz. In addition, the proposed variable resolution method can also be applied to the development of multi-axis F/T sensors.

关键词： Mechanical sensors Vibrations Analytical models Torque Sensor phenomena and characterization Strain measurement Shock absorbers

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Fast Wheeled Driving to Legged Leaping onto a Step in a Leg-Wheel Transformable Robot

Fast Wheeled Driving to Legged Leaping onto a Step in a Leg-...

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IEEE international conference on robotics and automation (ICRA)

作者： Zhi-Ren Chen Wei-Shun Yu Pei-Chun Lin Department of Mechanical Engineering National Taiwan University (NTU) Taiwan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The leg-wheel transformable robot has the advantage of smooth, fast, and power-efficient motion on flat terrain and negotiability on rough terrain. This study presents a highly dynamic maneuver of the robot to leap onto a step using its legged form from its original form of wheeled driving, taking full advantage of the rapid switching capabilities of the leg-wheel design of the robot. The robot motion is designed based on a reduced-order model and is planned using an optimization method with multiple constraints. In addition, both position and impedance control strategies are investigated. The proposed strategy is experimentally evaluated. The results show that the robot can leap onto a step higher than itself and then smoothly transition back to the wheeled mode after leaping. The dynamic driving-to-leaping maneuver endows the robot with an alternative and time-efficient approach to negotiate the step obstacles.

关键词： Legged locomotion Robot motion Dynamics Optimization methods Switches Reduced order systems Impedance

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SPOTLIGHT: MOBILE UI UNDERSTANDING USING VISION-LANGUAGE models WITH A FOCUS 11

SPOTLIGHT: MOBILE UI UNDERSTANDING USING VISION-LANGUAGE MOD...

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11th international conference on Learning Representations, ICLR 2023

作者： Li, Gang Li, Yang Google Research Mountain ViewCA United States

Mobile UI understanding is important for enabling various interaction tasks such as UI automation and accessibility. Previous mobile UI modeling often depends on the view hierarchy information of a screen, which directly provides the structural data of the UI, with the hope to bypass challenging tasks of visual modeling from screen pixels. However, view hierarchies are not always available, and are often corrupted with missing object descriptions or misaligned structure information. As a result, despite the use of view hierarchies could offer short-term gains, it may ultimately hinder the applicability and performance of the model. In this paper, we propose Spotlight, a vision-only approach for mobile UI understanding. Specifically, we enhance a vision-language model that only takes the screenshot of the UI and a region of interest on the screen-the focus-as the input. This general architecture of Spotlight is easily scalable and capable of performing a range of UI modeling tasks. Our experiments show that our model establishes SoTA results on several representative UI tasks and outperforms previous methods that use both screenshots and view hierarchies as inputs. Furthermore, we explore multi-task learning and few-shot prompting capacities of the proposed models, demonstrating promising results in the multi-task learning direction. © 2023 11th international conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Image segmentation

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